Abstract
With limited bed availability at Malta’s state-run rehabilitation hospital, efficient and accurate assessment of frail older adults is essential for appropriate inpatient geriatric rehabilitation (GR) admissions. This study aimed to identify key predictors influencing admission decisions for inpatient GR programmes in Malta. Prospective data were collected from 250 patients aged 60 and over. In total, 83 predictors, including demographic details, clinical history, functional levels and social factors, were analysed against admission outcomes. Logistic regression analysis, mainly univariate analysis, followed by multivariate analysis with backward stepwise elimination, was used to determine significant predictors. Seven key predictors were identified, which shed light on the decision-making processes of the assessing specialists, highlighting the importance of embracing the ICF model rather than relying solely on medical criteria. These predictors reflect the WHO ICF and CGA frameworks, supporting a holistic, patient-centred approach. They informed the development of a digital tool, incorporating a future predictive model to guide GR admission decisions in Malta, aiming to facilitate the delivery of stratified and personalised care by enabling clinicians to select a care pathway based on the patient’s potential health trajectory in response to rehabilitation interventions.
1. Introduction
As the population in Malta continues to age, acute hospitals are witnessing a rise in admissions of frail older adults [1]. Frailty, an age-related condition, manifests as general weakness and fatigue, leading to reduced physical activity; it affects balance, gait, functional abilities, and cognition [2,3]. Frail older adults experience a decline in physiological systems, making it difficult for their bodies to meet the demands placed upon them. Conducting a comprehensive assessment would allow clinicians to identify the diverse needs of frail older adults as effectively as possible, enabling the provision of tailored services that would best address their requirements [2,3,4,5].
Malta’s healthcare system comprises a state-operated acute hospital serving the entire Maltese population, alongside a state-run rehabilitation hospital with coverage for the entire adult population. This rehabilitation hospital is a 293-bed facility specialising in adult rehabilitation, employing a comprehensive, multidisciplinary approach to inpatient rehabilitation [6]. It is important to note that it administers its services not only to the geriatric demographic, but also to all adults aged 18 and over who might require intensive rehabilitation. As a result, the number of available beds remains consistently limited. In light of this constraint in resources, it becomes imperative to adopt a more discerning patient-assessment process, ensuring more efficiency and an enhanced quality of service provided.
In view of the above, the aim of this study was to find which predictors are the most determining when assessing for potential admission to inpatient geriatric rehabilitation (GR) in Malta.
2. Materials and Methods
2.1. Data Source
A total of 2683 older adults were referred to inpatient geriatric rehabilitation in Malta in 2021. During the study planning stage, the anticipated number of potential predictors and the requirements for multivariable logistic regression were considered. The initial assessment identified 83 potential predictors, corresponding to more than 123 parameters when categorical variables were taken into account. Using the criterion of 10 events per variable (EPV), the required number of outcome events would have exceeded the number of patients realistically available within the eligible population. Among the 2683 referrals, 937 patients were accepted for inpatient GR, corresponding to approximately 7.62 EPV.
Given the finite population available and the practical constraints on recruitment, a conventional population-based sample size calculation was performed, assuming a 95% confidence interval with a 6% margin of error, which yielded a required sample size of 243 patients. Data was collected prospectively from 250 patients over the age of 60, who were referred for potential admission to inpatient GR, once discharged from the main acute hospital in Malta. These referrals were carried out between March 2023 and June 2023. The patients referred were informed about the study by the respective consultant geriatrician or rehabilitation specialist who was assessing them. If the patients expressed interest or required additional information, the researcher approached them to provide further explanation, along with an information sheet. The first 250 patients who met the criteria of being over the age of 60 and who agreed to take part were included in the study.
Upon obtaining informed consent, these patients were assessed using an assessment tool devised for this research. This assessment covered various aspects, such as demographic information, clinical history, presenting symptoms and diagnosis, medication, previous and current levels of function, social situation, and more. The researcher also noted the outcome of interest (end point) which, in this case, was whether the patient was accepted into inpatient GR or not, following the consultants’ assessment at that point in time.
2.2. Ethical Considerations
This study was granted a favourable ethical opinion by the research ethics committees of the local university, local acute hospital and local rehabilitation hospital.
To decrease selection bias, the patients were identified through intermediaries, and to ensure confidentiality, each participant was assigned a code, known only to the researcher, to safeguard their identity. Participants were also informed that they could withdraw their participation without any penalty or consequence, prior to signing the consent form. The information sheets also stated the regulations of GDPR and national legislation, which gave each participant the right to access, correct and even ask for any data concerning them to be erased. The information sheet and the consent form were both available in Maltese and English, and participants were asked which language they preferred.
The assessment information obtained during the study was stored securely on an electronic device. To ensure data protection, the assessments were password-protected and stored in an encrypted format, maintaining the promised confidentiality regarding the participants’ information and protection of their privacy.
2.3. Predictors
The comprehensive geriatric assessment (CGA) is a well-established care procedure that encompasses a comprehensive evaluation of older adults, taking into account various aspects of their well-being [7]. It is similar to the International Classification of Functioning, Disability and Health (ICF), a framework developed by the World Health Organization (WHO) in 2001. The WHO ICF classifies health and health-related domains, which assists clinicians in identifying any changes in the body structure and functioning levels of a person. This is achieved by adopting a holistic approach, looking at the person’s individual societal perspectives that might affect functioning [8]. The CGA goes beyond solely examining a patient’s state of health and considers other factors that influence their quality of life and overall well-being. Additionally, it takes into consideration the impact on the patient’s relatives or caregivers, if present [9]. The CGA is a continuous assessment that is reviewed regularly to monitor the patient’s progress and ensure their needs are addressed and adjusted accordingly.
Based on this premise, this research aimed to identify which predictors influenced the potential admission to inpatient GR programmes in Malta. The predictors were identified through two main pathways. Expert-panel sessions were carried out, during which local panellists identified the key clinical characteristics they deemed essential for evaluating patients for inpatient GR programmes in Malta [10]. These consensus features were compared with findings from a scoping review that was carried out to identify which assessment tools or outcome measures are used to assess patients for potential admission to GR programmes [11]. The final components were integrated into a comprehensive assessment tool, initially developed in a paper-based format. This tool included 83 predictors, each of which was individually analysed to determine its impact on the likelihood of a patient benefiting from an inpatient GR programme. The goal was to identify which of these predictors most significantly influenced the consultants’ decision-making processes.
2.4. Outcomes
In the current approach, the consultant geriatrician accepts or denies patients to inpatient GR programmes based on past clinical experiences and expertise. For this data-analysis process, the consultant’s decision on rehabilitation was considered to be the outcome variable. Patients were assessed by consultant geriatricians and were either accepted into inpatient GR or not. This outcome was subsequently analysed in relation to the identified patient characteristics and clinical predictors to determine their association with the consultant’s decision.
2.5. Analysis
A logistic regression process was carried out for each stated outcome variable using SPSS (version 25). This type of regression was chosen as the dependent variable was dichotomous (binary outcome).
To minimise overfitting, all the independent variables were primarily compared with the dependent rehabilitation outcome (accepted/not accepted for inpatient GR) individually, to evaluate their significance. The independent-sample t-test and the Mann–Whitney test were used to compare the mean scores between two rehabilitation outcomes (accepted/not accepted for inpatient GR). For both tests, the null hypothesis specified that the scores varied marginally between the two groups and was valid if the p-value exceeded the 0.05 level of significance.
On the other hand, the chi-squared test was used to investigate the association between two categorical variables. One of these variables always provided the rehabilitation outcome (accepted/not accepted for inpatient GR), while the other variable provided demographic, medical, physical, functional, or social information about the patients assessed. The null hypothesis held when there was no association between the two variables and was valid if the p-value exceeded the 0.05 level of significance.
Upon completing the above steps, the next step was looking at univariable associations between each potential predictor and the outcome. The major limitation of the chi-squared test, the independent-samples t-test, and the Mann–Whitney test is that they simply investigate the relationship between a dependent variable (rehabilitation admission decision) and a lone predictor. However, this study aimed to estimate the collective effect of the predictors upon the corresponding dependent variable they influence. It is widely acknowledged that a lone predictor could become a very important contributor in explaining variations in the outcome but would be rendered unimportant in the presence of other predictors [12,13]. In other words, the suitability of a predictor often depends on other predictors in the same set. Given the exploratory objective of the study, following the elimination of insignificant variables during univariate analysis, multivariable logistic regression was undertaken to examine the independent associations between the candidate predictors and the consultant’s decision regarding admission to inpatient GR.
Backward elimination, using a p-value greater than 0.05 as the removal criterion, was used as an exploratory variable-selection approach to obtain a parsimonious model from the available candidate predictors. This approach was not intended to constitute the development or validation of a clinical prediction model, but rather to identify potentially important predictors and assess whether the findings provided sufficient justification for the development of a future prediction model using a larger dataset [14,15].
3. Results
The assessment that was used to gather patient data was composed of 83 variables, which were analysed individually to establish the statistical significance of each. Variables that could be expressed as both continuous and categorical measures were analysed as continuous variables based on their numerical scores (e.g., the MoCA score), rather than their corresponding categorical classifications. Among these variables, a total of 44 proved to have a significant association with the ‘accepted to inpatient GR’ statement. The significant variables are shown in Table 1.
Table 1.
Significant variables (potential predictors).
Using these 44 variables, a logistic regression model was fitted to relate the binary outcome, that is, the rehabilitation outcome (accepted/not accepted for inpatient GR), to several predictors that qualified as ‘significant’ when analysed individually. Hence, one model was created for all the predictors together, using an iterative backward elimination process, where variables are removed one at a time, the model is refitted, and the process is repeated until all remaining variables are statistically significant. The variables were fitted into the model, seven of which proved to be statistically significant in the end. These seven variables were: (1) the patient’s MoCA score; (2) the previous level of mobility; (3) the reason for admission to the acute hospital; (4) if the patient is a smoker or non-smoker; (5) the ability to transfer from lying to sitting; (6) the patient’s living arrangements; and (7) the relatives’ plans for the patient following discharge. These are shown in Table 2.
Table 2.
Likelihood-ratio tests for the overall assessment.
The relatives’ plan for the patient following discharge was the best predictor of the rehabilitation outcome (accepted/not accepted for inpatient GR). This was followed by the reason for admission to the acute hospital, the ability to transfer from lying → sitting, the patient not living alone, smoking status, previous level of physical mobility, and the patient’s MoCA score (assessing cognitive ability).
When interpreting the parameter estimates of these variables, it became evident that for every 1 unit increase in the patient’s MoCA score, the odds of being accepted, rather than rejected, increased by 3.5%. On the other hand, for every 1 unit increase in the patient’s previous mobility dependency, the odds of being accepted, rather than rejected, diminished by 17.4%.
Upon interpreting the categorical variables, it was clear that non-smokers were more likely to be accepted than smokers. Participants being admitted to the acute hospital following a fall with fracture, patients having fallen without incurring fracture, patients with respiratory problems, or patients requiring amputation or other general surgery were more likely to be admitted to inpatient GR than patients suffering from neurological problems or deconditioning. On the other hand, patients who could not transfer from lying to sitting independently were more likely to be accepted, over those who could carry out this task alone. Patients who lived with a partner or sibling of similar age were more likely to be accepted than those who lived in a residential home, had a live-in carer, or lived with their children. Lastly, those patients whose relatives planned to make it possible for the patient to return home following discharge were more likely to be accepted to inpatient GR than those who had no relatives, or whose relatives were undecided, or planned to admit the patient to a residential home.
4. Discussion
The admission process for inpatient GR is inherently complex. While guidelines such as the gold standard CGA and the more recent ICF provide a framework, standardisation remains lacking. Ultimately, consultants must rely on clinical judgement to assess a patient’s potential—a challenging task in the context of frailty, where prognostication is often uncertain and subject to debate [16,17]. This study aimed to identify the most influential predictors of rehabilitation potential that influence admission to inpatient GR programmes in Malta, based on the clinical judgement of the assessing consultants.
It is reasonable to assume that if a consultant were presented with all 83 predictors, they could make more informed decisions regarding a patient’s likelihood of benefitting from inpatient GR programmes. However, the assessment of 83 predictors requires over an hour to complete, rendering the process inefficient and impractical in clinical settings. By examining statistical significance, this study aimed to identify the shortest possible list of predictors while preserving clinical effectiveness. The final seven predictors identified shed light on the decision-making processes of the assessing specialists, highlighting their holistic approach to patient assessment and underscoring the importance of embracing the ICF model rather than relying solely on medical criteria. Notably, these seven predictors encompass multiple domains that contribute to a comprehensive understanding of the individual.
None of the identified predictors were demographic; specialists did not differentiate between factors such as gender, age, locality, or socioeconomic status in their decision-making processes. Variables such as housing conditions or educational background had no bearing on admission decisions. Instead, the most influential factor was the level of social support available to the patient. Notably, the strongest predictor of admission was the relatives’ post-discharge plan for the patient—an influence that outweighed even the patient’s own preferences.
In Malta’s strong familial culture, relatives play a pivotal role in discharge planning, often exerting greater influence over rehabilitation decisions than the patients themselves [18]. While this may seem concerning, it becomes more apparent when considering the potential consequences of family disengagement. If relatives choose to withdraw support, the patient may face social isolation and an inability to manage independently at home, increasing the likelihood of institutionalisation. This dynamic highlights the significant gap in public and patient awareness regarding the nature and scope of GR. Many individuals remain unfamiliar with its purpose and the options available for care, which may lead to premature or uninformed decisions about care-withdrawal or long-term care [17,19]. Furthermore, patients living with individuals of a similar age were more likely to be accepted into GR, possibly due to an implicit assumption that they would need to regain greater functional independence to return home.
The assessing consultants also referred to the patient’s medical profile, including the nature of the acute episode leading to hospitalisation (reason for admission) and their smoking status. Geriatricians were more likely to admit patients for inpatient GR if they were admitted following a fall with/without fracture, required amputation or other general surgery, or presented with respiratory problems, compared to patients suffering from neurological problems, deconditioning, or cognitive decline. This preference may stem from the anticipated decline in physical function among these patients if left without inpatient GR intervention. Interestingly, pre-existing health conditions and disease severity did not emerge as significant findings. Conversely, smoking status suggested that smokers were less likely to be admitted to inpatient GR, possibly due to increased comorbidities and health implications associated with smoking.
Cognitive function also emerged as a crucial aspect in understanding rehabilitation potential, with the MoCA test proving to be a strong predictor among the final seven predictors identified. This is consistent with the existing literature, indicating that patients with cognitive impairment may be considered poor candidates for rehabilitation, despite having other positive predictors [16,20,21,22].
The relationship between the patient’s medical status and functional abilities was noted, with a focus on assessing changes in mobility and transfer capabilities, post-hospitalisation. The final predictors that evaluated function were: (1) the patient’s previous level of mobility, where the more dependent the patient was before hospitalisation, the less likely they were to be admitted, and (2) the patient’s current ability to transfer from supine to sitting up in bed. Patients who required assistance to sit up independently were more likely to be accepted into inpatient GR than those who could do so independently.
The geriatricians’ thought process aimed to adopt a holistic approach to patient assessment by incorporating key domains outlined in both the ICF and CGA. Despite having a small number of predictors, collectively, they still provided a comprehensive overview, capturing essential aspects of the patient’s medical condition, level of support, and overall suitability for inpatient GR. Similar to the ICF framework, they focus on evaluating a patient’s potential to participate in society while living with an impairment [8,23,24].
Establishing an individual’s rehabilitation potential is considered a process that requires complex clinical judgement and prognostication of the likelihood of the benefit to be reaped from rehabilitation programmes. The final seven predictor variables observed the principle that a GR assessment should include a holistic overview of the patient, including the physical (pre- and post-hospitalisation level of function), neuro-psychological (cognition), and social (home environment, availability of social support and willingness of carers to provide support) aspects concerning the patient. Conducting a standardised assessment would allow clinicians to identify the diverse needs of frail older adults as effectively as possible, maximising patient-and-carer involvement, enabling the provision of tailored services that would best address their requirements, for a more patient-centred model of care [2,3,4,5].
4.1. Strengths and Limitations
Using existing data is a faster and more efficient means of acquiring information, especially in view of the abundance of data accessible through electronic health records (EHRs), which facilitates meeting the necessary sample size requirements. However, in this specific case, using existing data was not feasible due to the absence of a dataset that encompassed all the predictors required for the study. The limited locally available data suffered from poor quality, with crucial predictors going potentially unrecorded. The expert panel and scoping review, held as groundwork for this research, identified numerous variables not routinely documented. Additionally, the data, which was mainly available in paper form, caused a substantial information shortfall. Therefore, the main strength of this study was that new data was collected for the model. This ensured that the population of interest was captured within the right setting, specifying the outcome and predictors, and thus securing better quality of data, ensuring no missing data was present in the dataset.
Inputting too many predictors into the model at once increases the risk of obtaining results by chance (overfitting), which could bring about shortcomings in predictive performance when applying the model to new patients. Hence, univariable screening was undertaken before multivariable analysis, to eliminate insignificant predictors. However, this could also pose challenges, as it fails to assess the independent or added value of each variable after considering other variables in the analysis. For instance, a p-value below 0.05 from a univariable analysis holds less significance than a similar p-value from a multivariable analysis. This might mean that it could rule out potentially important predictors due to cofounders that might not be accounted for. Therefore, it was deemed necessary to find a balance between predictors while avoiding overfitting by using backward stepwise elimination in the model. Future studies could complement the above-mentioned domain-based selection strategy with formal multicollinearity diagnostics to further assess model stability.
The potential for sample-dependent variable selection and model instability associated with automated variable selection was recognized and considered a limitation. The sample size used in this phase was considerably small and it increased uncertainty in the model, thus underscoring the importance of cautious interpretation and consideration of model performance. Nevertheless, it helped create a guideline on how to apply it to a bigger sample in future research.
4.2. Future Developments
The final predictive model was based on clinicians’ decisions regarding whether patients should be admitted to inpatient GR programmes and therefore reflects clinical judgement rather than subsequent rehabilitation outcomes. It did not assess whether patients ultimately benefitted from rehabilitation or the extent to which their functional and clinical outcomes improved following admission. Future longitudinal studies should therefore evaluate the relationship between the identified predictors and outcomes following rehabilitation. Such studies would enable further validation and refinement of the model and provide a stronger basis for its clinical application.
Additionally, a larger sample size and the continuous involvement of clinicians would help to improve the model, being built on clinical knowledge and compiled patient data.
5. Conclusions
The identification of optimal assessments, outcome measures and predictors to quantify GR accurately proved to be very challenging. Assessments to evaluate rehabilitation potential in older adults living with frailty need to be holistic, encompassing medical, physical, functional, and cognitive components, as well as considering the desires and future plans of the patients and their relatives. These elements, along with pre-admission and current abilities, contribute to a more accurate prediction of recovery and the type of response to rehabilitation.
GR potential assessments must reflect the principles of WHO ICF and CGA approaches to care, which advocate for holistic, multidisciplinary, and patient-centred approaches. This study constitutes the first attempt at identifying which predictor variables could best inform potential admission to inpatient GR programmes in Malta. The findings provide an initial evidence base for identifying potentially relevant predictors that may inform the future development of a digital health tool that incorporates an integrated prediction model. Further research using a larger dataset, appropriate model development methodology and independent validation would be required for such a tool to be considered for clinical use. Future work will therefore evaluate the feasibility and potential utility of integrating these findings into a digital framework to support clinicians in GR assessment and clinical decision-making.
Author Contributions
F.M. conceived and designed the study, with support and guidance from the academic supervisors S.L.M. and C.A. Data acquisition and screening was done by F.M. and analysed by S.L.M. The manuscript was written by F.M.; S.L.M., P.F. and C.A. were involved in the critical revision of the manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
This research work has been funded by the Tertiary Education Scholarship Scheme (TESS), offered by the Government of Malta, reference number MFED 758/2021/47, and awarded to FM for her Clinical Doctoral Research programme. The funding source had no role in the design and conduct of the study, data collection and analysis, preparation, review or approval of the manuscript.
Institutional Review Board Statement
The study was conducted in accordance with the Decla-ration of Helsinki, and approved by the Institutional Review Board of the Faculty Research Ethics Committee, Faculty of Health Sciences, University of Malta (protocol code FHS-2021-00031 and date of approval 16 May 2022).
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
The relevant data supporting the conclusions of this study is included within the article. The datasets used and analysed during the study are available from the corresponding author, on request.
Acknowledgments
The authors wish to thank all the clinicians and patients for their participation and interest shown in this study.
Conflicts of Interest
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
Abbreviations
The following abbreviations are used in this manuscript:
| CGA | Comprehensive Geriatric Assessment |
| EHR | Electronic health record |
| EPV | Events per variable |
| GDPR | General Data Protection Regulation |
| GR | Geriatric rehabilitation |
| IADLs | Instrumental activities of daily living |
| ICF | International Classification of Functioning, Disability and Health |
| MoCA | Montreal Cognitive Assessment |
| PADLs | Personal activities of daily living |
| WHO | World Health Organization |
References
- National Statistics Office Malta. Census of Population and Housing 2021: Final Report: Population, Migration and Other Social Characteristics; National Statistics Office Malta: Valletta, Malta, 2023; Volume 1, ISBN 978-9918-605-14-9.
- Westgård, T.; Ottenvall Hammar, I.; Holmgren, E.; Ehrenberg, A.; Wisten, A.; Ekdahl, A.W.; Dahlin-Ivanoff, S.; Wilhelmson, K. Comprehensive geriatric assessment pilot of a randomized control study in a Swedish acute hospital: A feasibility study. Pilot Feasibility Stud. 2018, 4, 41. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Wilhelmson, K.; Andersson Hammar, I.; Ehrenberg, A.; Niklasson, J.; Eckerblad, J.; Ekerstad, N.; Westgård, T.; Holmgren, E.; Åberg, N.D.; Dahlin Ivanoff, S. Comprehensive Geriatric Assessment for Frail Older People in Swedish Acute Care Settings (CGA-Swed): A Randomised Controlled Study. Geriatrics 2020, 5, 5. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Pritchard, J.M.; Kennedy, C.C.; Karampatos, S.; Ioannidis, G.; Misiaszek, B.; Marr, S.; Patterson, C.; Woo, T.; Papaioannou, A. Measuring frailty in clinical practice: A comparison of physical frailty assessment methods in a geriatric out-patient clinic. BMC Geriatr. 2017, 17, 264. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Morley, J.E.; Vellas, B.; van Kan, G.A.; Anker, S.D.; Bauer, J.M.; Bernabei, R.; Cesari, M.; Chumlea, W.C.; Doehner, W.; Evans, J.; et al. Frailty consensus: A call to action. J. Am. Med. Dir. Assoc. 2013, 14, 392–397. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- health.gov.mt. Government of Malta Website. Fact Sheet: Karin Grech Rehabilitation Hospital. 2021. Available online: https://healthservices.gov.mt/en/karin-grech-hospital/Pages/home.aspx (accessed on 25 July 2026).
- Ellis, G.; Gardner, M.; Tsiachristas, A.; Langhorne, P.; Burke, O.; Harwood, R.H.; Conroy, S.P.; Kircher, T.; Somme, D.; Saltvedt, I.; et al. Comprehensive geriatric assessment for older adults admitted to hospital. Cochrane Database Syst. Rev. 2017, 2017, CD006211. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- World Health Organization. Towards a Common Language for Functioning, Disability and Health: ICF; WHO: Geneva, Switzerland, 2002. [Google Scholar]
- BGS. Comprehensive Geriatric Assessment Toolkit for Primary Care Practitioners; British Geriatrics Society: London, UK, 2019; pp. 1–48. Available online: https://www.bgs.org.uk/sites/default/files/content/resources/files/2019-03-12/CGA%20Toolkit%20for%20Primary%20Care%20Practitioners_0.pdf (accessed on 25 July 2026).
- Muscat, F.; Camilleri, L.; Attard, C.; Lungaro Mifsud, S. Inpatient Geriatric Rehabilitation: Definitions and Appropriate Admission Criteria, as Established by Maltese National Experts. J. Clin. Med. 2022, 11, 7230. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Muscat, F.; Camilleri, L.; Attard, C.; Lungaro Mifsud, S. Assessment Tools for the Admission of Older Adults to Inpatient Rehabilitation: A Scoping Review. J. Clin. Med. 2023, 12, 919. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Sauerbrei, W.; Perperoglou, A.; Schmid, M.; Abrahamowicz, M.; Becher, H.; Binder, H.; Dunkler, D.; Harrell, F.E., Jr.; Royston, P.; Heinze, G. State of the art in selection of variables and functional forms in multivariable analysis—Outstanding issues. Diagn. Progn. Res. 2020, 4, 3. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Royston, P.; Sauerbrei, W. Multivariable Model-Building: A Pragmatic Approach to Regression Analysis Based on Fractional Polynomials for Modelling Continuous Variables, 1st ed.; John Wiley Sons Ltd.: Hoboken, NJ, USA, 2008. [Google Scholar] [CrossRef] [Scilit]
- Moons, K.G.; Altman, D.G.; Reitsma, J.B.; Ioannidis, J.P.; Macaskill, P.; Steyerberg, E.W.; Vickers, A.J.; Ransohoff, D.F.; Collins, G.S. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): Explanation and elaboration. Ann. Intern. Med. 2015, 162, W1–W73. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Collins, G.S.; Reitsma, J.B.; Altman, D.G.; Moons, K.G. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement. Br. J. Cancer 2015, 112, 251–259. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Wade, D.T. Rehabilitation potential: A critical review of its meaning and validity. Clin. Rehabil. 2022, 37, 869–875. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Cowley, A.; Goldberg, S.E.; Gordon, A.L.; Logan, P.A. Rehabilitation potential in older people living with frailty: A systematic mapping review. BMC Geriatr. 2021, 21, 533. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Vella, S.; Tabone, C.; Galea Curmi, E. Families. In Social Policy in Malta; Vella, S., Galea Curmi, E., Eds.; Midsea Books: Santa Venera, Malta, 2022; pp. 316–341. [Google Scholar]
- Grund, S.; Gordon, A.L.; van Balen, R.; Bachmann, S.; Cherubini, A.; Landi, F.; Stuck, A.E.; Becker, C.; Achterberg, W.P.; Bauer, J.M.; et al. European consensus on core principles and future priorities for geriatric rehabilitation: Consensus statement. Eur. Geriatr. Med. 2020, 11, 233–238. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Goodwin, V.A.; Allan, L.M. ‘Mrs Smith has no rehab potential’: Does rehabilitation have a role in the management of people with dementia? Age Ageing 2019, 48, 5–7. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Wade, D.T. Defining rehabilitation: An exploration of why it is attempted, and why it will always fail. Clin. Rehabil. 2021, 35, 1650–1656. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Yohannes, A.; Muir, S. The Impact of Cognitive Impairment on Rehabilitation Outcomes in Elderly Patients Admitted with a Femoral Neck Fracture: A Systematic Review. J. Geriatr. Phys. Ther. 2009, 32, 24–32. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Gladman, J. The International Classification of Functioning, Disability and Health and its value to rehabilitation and geriatric medicine. J. Chin. Med. Assoc. 2008, 71, 275–278. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Allan, C.M.; Campbell, W.N.; Guptill, C.A.; Stephenson, F.F.; Campbell, K.E. A conceptual model for interprofessional education: The international classification of functioning, disability and health (ICF). J. Interprofessional Care 2006, 20, 235–245. [Google Scholar] [CrossRef] [Scilit] [PubMed]
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