Search Results (95)

Background and Objectives: Vaginal natural orifice transluminal endoscopic surgery, vNOTES, has become an increasingly preferred minimally invasive option for benign hysterectomy. General anesthesia is still the routine choice, yet regional methods such as combined spinal epidural anesthesia may support a smoother postoperative course. Although the use of vNOTES is expanding, comparative information on anesthetic approaches remains limited, and its unique physiologic setting requires dedicated evaluation. To compare combined spinal epidural anesthesia with general anesthesia for benign vNOTES hysterectomy, focusing on postoperative nausea and vomiting, recovery quality, and intraoperative physiologic safety. Materials and Methods: This retrospective cohort study was conducted in a single center and identified women who underwent benign vNOTES hysterectomy between March 2024 and August 2025 from electronic medical records. Participants received either combined spinal epidural anesthesia or general anesthesia according to routine clinical practice. All patients were managed within an enhanced recovery pathway that incorporated standardized analgesia and prophylaxis for postoperative nausea and vomiting. The primary outcome was the incidence of postoperative nausea and vomiting during the first day after surgery. Secondary outcomes included time to discharge from the recovery unit, pain scores at set postoperative intervals, early functional recovery, patient satisfaction and physiologic parameters extracted from intraoperative monitoring records. Analyses were performed according to the anesthesia group documented in the medical files. Results: One hundred forty patients met inclusion criteria and were included in the analysis. Combined spinal epidural anesthesia was linked to a lower incidence of postoperative nausea and vomiting, a shorter stay in the post-anesthesia care unit, and reduced pain scores in the first 24 h (adjusted odds ratio 0.32, ninety five percent confidence interval 0.15 to 0.68). Early ambulation and oral intake were reached sooner in the combined spinal epidural group, with higher overall satisfaction also noted. Adherence to ERAS elements was similar between groups, with no meaningful differences in early feeding, mobilization, analgesia protocols or PONV prophylaxis. During the procedure, combined spinal epidural anesthesia produced more episodes of hypotension and bradycardia, while general anesthesia was linked to higher airway pressures and lower oxygen saturation. Complication rates within the first month were low in both groups. Conclusions: In this observational cohort study, combined spinal epidural anesthesia was associated with lower postoperative nausea, earlier recovery milestones and greater patient comfort compared with general anesthesia. Hemodynamic instability occurred more often with neuraxial anesthesia but was transient and manageable. While these findings point to potential recovery benefits for some patients, the observational nature of the study and the modest scale of the differences necessitate a cautious interpretation. They should be considered exploratory rather than definitive. The choice of anesthesia should therefore be individualized, weighing potential recovery benefits against the risk of transient hemodynamic effects. Larger and more diverse studies are needed to better define patient selection and clarify the overall risk benefit balance. These findings should be interpreted cautiously and viewed as hypothesis-generating rather than definitive evidence supporting one anesthetic strategy over another. Full article

Peri-implant infections pose a significant challenge in dental implantology. This study aimed to develop and characterize a chitosan–vancomycin coating for titanium surfaces, focusing on drug loading, release kinetics, antimicrobial performance, and cytocompatibility. Grade 4 titanium discs were coated with a chitosan film using the dip-coating technique and subsequently loaded with vancomycin through immersion in an aqueous solution. Coating morphology was examined by scanning electron microscopy (SEM). Vancomycin loading was quantified by spectrophotometry, and release kinetics were monitored over 144 h (6-day). Antimicrobial activity was assessed through agar diffusion assays against Staphylococcus aureus. Cytocompatibility was evaluated using human mesenchymal stem cells (hMSCs), whose metabolic activity, adhesion, and morphology were assessed over a 19-day culture period by resazurin assay and SEM. SEM analysis revealed a uniformly distributed, smooth, and crack-free chitosan film, which remained stable after drug loading. The coating exhibited a biphasic release profile, characterized by an initial burst followed by sustained release over six days, which maintained antimicrobial activity, as confirmed by inhibition zones. hMSCs adhered and proliferated on the coated surfaces, displaying normal morphology despite a transient reduction in metabolic activity on vancomycin-containing films. These findings support the potential of chitosan–vancomycin coatings as localized antimicrobial strategies for implant applications, warranting further in vivo and mechanical evaluations. Full article

Background and Purpose: Adherence to oral anticancer therapy correlates with outcome. Lenalidomide (LEN) is an oral mainstay treatment for multiple myeloma (MM), administered in 21-day/7-day (on/off) cycles. Data on LEN adherence is limited. Electronic monitoring (EM) represents the most reliable adherence assessment method. Experimental Approach: We conducted a prospective observational study using electronic medication event monitoring (MEMS^®) in lenalidomide-naïve multiple myeloma patients to quantify adherence during on/off cycles and identify patterns of non-adherence in real-world practice. On and off cycles were determined semi-automatically. Implementation adherence was calculated as the proportion of prescribed drug taken, during each on cycle and across all on cycles. Daily adherence predictors were analyzed using logistic regression with generalized estimating equations. Key Results: Eighty-five patients were included. Median age was 68 years, 66% received LEN as a second-line treatment, 75% of patients perfectly adhered to the recommended 21/7-day on/off cycle. Median implementation adherence was 100%. Only 4% of patients had a proportion of doses taken below 90%. All doses were taken by 51% of patients, while 9% missed ≥4 doses. Among the 13 predictors investigated, only age under 80 and participation in a support group were statistically significant. Conclusions: this novel assessment of LEN adherence in MM patients demonstrated high implementation adherence and cycle duration compliance. Full article

Chronic lower-extremity wounds in patients undergoing exoskeleton-assisted rehabilitation require materials that can both protect tissue and enable real-time physiological monitoring. Conventional dressings lack dynamic sensing capability, while current conductive hydrogels often compromise either adhesion or electronic performance. Here, we present a biointegrated hydrogel (CPSD) composed of carboxymethyl chitosan (CMCS) and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) forming the conductive backbone, integrated with dopamine-functionalized sodium alginate (SD); the network is assembled via electrostatic complexation and carbodiimide (EDC/NHS)-mediated covalent crosslinking. The resulting hydrogel exhibits a dense, tissue-conformal porous network with tunable swelling, stable mechanical integrity, and high photothermal conversion efficiency. In vitro assays confirmed potent antioxidant activity, strong antibacterial performance (>90% under near-infrared), and excellent cytocompatibility and hemocompatibility. CPSD shows bulk conductivity ~1.6 S·m⁻¹, compressive modulus ~15 kPa, lap-shear adhesion on porcine skin ~9.5 kPa, and WVTR ~75 g·m⁻²·h⁻¹, supporting stable biointerfaces for motion/sEMG sensing. Integrated into a lower-limb exoskeleton, CPSD hydrogels adhered securely during motion and reliably captured electromyographic and strain signals, enabling movement-intent detection. These results highlight CPSD hydrogel as a multifunctional interface material for next-generation closed-loop rehabilitation systems and mobile health monitoring. Full article

Background/Objectives: One of the most important self-management behaviors is following agreed-upon treatment recommendations. In chronic disease, which affects over one-third of adults, a critical behavior is taking prescribed medication. However, approximately half of patients with chronic conditions fail to adhere to medication recommendations. Research into medication adherence is complicated by the diversity of measurement methods and definitions, resulting in inconsistent outcomes. Accurate measurement is essential for clinical decision making and identifying effective interventions. This state-of-the-art review aimed to map the current landscape of adherence measurement in chronic disease management and provide evidence-based recommendations for future research and practice. Methods: Using a state-of-the-art review approach, we examined objective and subjective adherence measures in studies where medication adherence was a primary outcome, published from August 2019 to July 2024. The frequencies of each method type were calculated. In studies using more than one method within a sample, adherence outcomes were compared to assess their comparability. Results: Of 1036 screened records, 314 met the inclusion criteria. Self-report questionnaires were most frequently used (72% of studies), followed by pharmacy refill measures (22%), electronic monitoring (2.5%), and biologic assays (1.3%). Subjective measures were more frequently used due to their convenience and lower cost but they reduce the level of precision. Objective measures offered greater precision but at a higher cost and logistical complexity. Conclusions: Our findings suggest a dominant reliance on subjective measures. Standardizing definitions, thresholds, and reporting, and adopting multimodal measurement strategies, will improve the validity, comparability, and clinical utility of adherence research. Full article

Hospital readmissions within 30 days of discharge are a key metric of healthcare quality and a major driver of cost. Accurate risk stratification enables targeted home care interventions, such as remote monitoring, timely nurse follow-up, and medication adherence programs, designed to mitigate preventable readmissions. Thus, we conducted a systematic comparison of structured electronic health record (EHR) data and unstructured discharge summaries for predicting 30-day unplanned readmissions. Using the MIMIC-IV database, we integrated admissions, emergency department records, laboratory values, and discharge notes, and we restricted the cohort to ED-based readmissions. Following rigorous preprocessing, we balanced the dataset and split it into training, validation, and test sets at the patient level. Structured features, including the LACE score (length of stay, acuity of admission, comorbidities, and ED utilization) and triage vitals, were modeled with logistic regression, random forest, XGBoost, and LightGBM. Discharge notes were analyzed with ClinicalLongformer to generate contextual embeddings and with Qwen2.5-7B-Instruct for few-shot classification. ClinicalLongformer achieved the best discrimination (AUROC = 0.72, F1 = 0.68), outperforming classical ML baselines (AUROC ≈ 0.65–0.67, F1 ≈ 0.64–0.65). The LLM (Qwen2.5-7B-Instruct) yielded moderate discrimination (AUROC = 0.66, F1 = 0.65) while providing interpretable chain-of-thought rationales. These findings highlight the value of narrative data for risk stratification and suggest that transformer-based language models have the potential to enable scalable, explainable early-warning systems to guide home care and prevent avoidable readmissions. Full article

Background: Effective dentist–patient communication is pivotal for quality dental care and patient satisfaction. Advances in technology, such as the application of digital dentistry, have modernized how dentists and patients interact. Objective: The objective of this study is to review traditional and modern communication methods and how the latter enhance patient engagement. Methods: A systematic search of PubMed, Scopus, and Web of Science (2000–2025) was conducted using keywords related to dental communication methods. Eligible studies underwent critical appraisal based on methodological clarity and relevance, and review quality was assessed using the SANRA framework. Results: While traditional methods remain foundational, digital advancements have significantly expanded communication channels. Teledentistry improves access through virtual consultations, electronic health records empower patients with transparency, and mobile apps facilitate convenient messaging and monitoring. Social media and interactive educational content enhance patient understanding and practice engagement. AI-driven tools further personalize patient interactions and automate administrative tasks. However, these modern strategies require careful implementation to ensure they meet clinical needs and adhere to strict privacy and security regulations. Conclusions: In conclusion, technological advancements have re-shaped dentist–patient communication, making it more flexible and efficient, thus enhancing patient engagement, satisfaction, and overall dental care quality. Future research should focus on conducting comparative and longitudinal studies to evaluate patient outcomes, satisfaction, and the long-term impact on the dentist–patient relationship across different hybrid communication models. Full article

Background: Physical activity is essential in the prevention and management of type 2 diabetes mellitus (T2D), yet adherence to recommended activity levels remains insufficient. Wearable electronic devices have emerged as tools to support physical activity through self-monitoring and enhanced user engagement. This review synthesizes current evidence on the effectiveness of wearable technologies in improving adherence to physical activity and promoting clinical and metabolic health in adults with T2D. Methods: The review was conducted using systematic search strategies in PubMed and Scopus. We included studies that involved the use of wearable devices to monitor physical activity for at least seven consecutive days. The reported outcomes were related to physical activity adherence or clinical–metabolic health. Thirty-two studies met the inclusion criteria and were analyzed in terms of study design, device type, intervention characteristics, and outcomes. Results: Wearable devices were used either for monitoring daily activity in free-living conditions or within structured, often supervised, interventions. Most studies reported increased physical activity, particularly in step count. Several studies showed improvements in blood pressure and lipid profile, while results for HbA1c and BMI were mixed. Structured interventions with behavioural support produced more consistent and clinically relevant outcomes than passive monitoring alone. Conclusions: Wearable technologies can support physical activity in adults with T2D, especially when integrated into structured behavioural programmes. From a clinical standpoint, they may serve as useful tools to enhance lifestyle adherence, particularly when combined with professional support. Their inclusion in care pathways could help personalize interventions and improve long-term self-management. Full article

Background: Electronic monitoring adherence devices (EAMDs) are increasingly being utilized in various healthcare settings to track medication adherence. Objective: To determine the accuracy of the Sensal Health MyAide™ Smart Doc in capturing dose removal from the vial, specifically the time of dose removal and the number of pills removed for each actuation of the device. Methods: This validation study compares the device’s recording of dose withdrawals from a prescription vial by simulated patients against reference documentation reported using MS Forms by the participants. Three participants completed a 4-day study consisting of two non-consecutive 1 h sessions per day encompassing six actuations from the prescription vial to be captured by the Sensal Health MyAide™ Smart Dock after their informed consent was obtained. Statistical analysis included percent agreement and Cohen’s kappa assessing agreement between user-reported data and electronic measurement data recorded by the MyAide™ Smart Dock. Outcome measures included confirmation of the specific user, time of dose removal (±1 min), and the number of pills withdrawn. Results: Three subjects were recruited to provide data for a total of 144 actuations. The study found perfect 100% agreement across the number of pills withdrawn and specific users withdrawing the pills and 99% agreement for the time of administration. The Cohen’s kappa values for the outcome measures were 1.00 (95%CI [1.00, 1.00]) for the number of pills dispensed and specific user and 0.993 (95%CI [0.990, 0.996]) for the time of administration. Conclusions: This study found that the Sensal Health MyAide™ Smart Dock can accurately record the time of administration, the number of pills dispensed, and the identity of the user dispensing the pills. Full article

Heart failure (HF) poses a substantial global burden due to its high morbidity, mortality, and healthcare costs. Accurate prognostication is crucial for optimizing treatment, resource allocation, and patient counseling. Prognostic tools range from simple clinical scores such as ADHERE and MAGGIC to more complex models incorporating biomarkers (e.g., NT-proBNP, sST2), imaging, and artificial intelligence techniques. In acute HF, models like EHMRG and STRATIFY aid early triage, while in chronic HF, tools like SHFM and BCN Bio-HF support long-term management decisions. Despite their utility, most models are limited by poor generalizability, reliance on static inputs, lack of integration into electronic health records, and underuse in clinical practice. Novel approaches involving machine learning, multi-omics profiling, and remote monitoring hold promise for dynamic and individualized risk assessment. However, these innovations face challenges regarding interpretability, validation, and ethical implementation. For prognostic models to transition from theoretical promise to practical impact, they must be continuously updated, externally validated, and seamlessly embedded into clinical workflows. This review emphasizes the potential of prognostic models to transform HF care but cautions against uncritical adoption without robust evidence and practical integration. In the evolving landscape of HF management, prognostic models represent a hopeful avenue, provided their limitations are acknowledged and addressed through interdisciplinary collaboration and patient-centered innovation. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

Background:Salmonella Typhimurium is a major zoonotic pathogen, in which type 1 fimbriae play a crucial role in intestinal colonization and immune modulation. This study aimed to improve the protective immunity of a previously developed growth-deficient strain—a double auxotroph for D-glutamate and D-alanine—by engineering the inducible expression of type 1 fimbriae. Methods: P_tetA-driven expression of the fim operon was achieved by λ-Red mutagenesis. fimA expression was quantified by qRT-PCR, and fimbriation visualized by transmission electron microscopy. Adhesive properties were evaluated through FimH sequence analysis, yeast agglutination, mannose-binding/inhibition assays, and HT-29 cell adherence. BALB/c mice were immunized orogastrically with IRTA ΔΔΔ or IRTA ΔΔΔ P_tetA::fim. Safety and immunogenicity were assessed by clinical monitoring, bacterial load, fecal shedding, ELISA tests, and adhesion/blocking assays using fecal extracts. Protection was evaluated after challenging with wild-type and heterologous strains. Results: IRTA ΔΔΔ P_tetA::fim showed robust fimA expression, dense fimbrial coverage, a marked mannose-sensitive adhesive phenotype and enhanced HT-29 attachment. Fimbrial overexpression did not alter intestinal colonization or translocation to mesenteric lymph nodes (mLNs). Immunization elicited a mixed IgG1/IgG2a, significantly increased IgA and IgG against type 1 fimbriae-expressing Salmonella, and enhanced the ability of fecal extracts to inhibit the adherence of wild-type strains. Upon challenge (IRTA wild-type/20220258), IRTA ΔΔΔ P_tetA::fim reduced infection burden in the cecum (−1.46/1.47-log), large intestine (−1.35/2.17-log), mLNs (−1.32/0.98-log) and systemic organs more effectively than IRTA ΔΔΔ. Conclusions: Inducible expression of type 1 fimbriae enhances mucosal immunity and protection, supporting their inclusion in next-generation Salmonella vaccines. Future work should assess cross-protection and optimize FimH-mediated targeting for mucosal delivery. Full article

In patients undergoing systemic treatment for cancer, symptom tracking via electronic patient-reported outcomes (ePROs) has been used to optimize communication and monitoring, and facilitate the early detection of adverse effects and to compare the side effects of similar drugs. We aimed to examine whether the patterns in electronic patient-reported outcomes, without any additional clinician data input, are predictive of the underlying cancer type and reflect tumor- and treatment-associated symptom clusters (SCs). The data were derived from a total of 226 patients who self-reported on the presence and severity (according to the Common Terminology Criteria for Adverse Events (CTCAEs)) of more than 90 available symptoms via the medidux^TM app (versions 2.0 and 3.2, developed by mobile Health AG based in Zurich, Switzerland). Among these, 172 had breast cancer as the primary tumor, 19 had lung, 16 had gut, 12 had blood–lymph, and 7 had prostate cancer. For this secondary analysis, a subgroup of 25 patients with breast cancer were randomly selected to reduce the risk of overfitting. The symptoms were aggregated by counting the days on which a particular symptom was reported, resulting in a symptom vector for each patient. A logistic regression model was trained to predict the type of the respective tumor from the symptom vectors, and the symptoms with coefficients above (0.1) were graphically displayed. The machine learning model was not able to recognize any of the patients with prostate and blood–lymph cancer, likely as these cancer types were barely represented in the dataset. The Area Under the Curve (AUC) values for the three remaining cancer types were breast cancer: 0.74 (95% CI [0.624, 0.848]); gut cancer: 0.78 (95% CI [0.659, 0.893]); and lung cancer: 0.63 (95% CI [0.495, 0.771]). Despite the small datasets, for the breast and gut cancers, the respective models demonstrated a fair predictive performance (AUC > 0.7). The generalization of the findings are limited especially due to the heterogeneity of the dataset. This line of research could be especially interesting to monitor individual treatment trajectories. Deviations in the electronic patient-reported symptoms from the treatment-associated symptom patterns could dynamically indicate treatment non-adherence or lower treatment efficacy, without clinician input or additional costs. Similar analyses on larger patient cohorts are needed to validate these preliminary findings and to identify specific and robust treatment profiles. Full article

Background: Saudi Arabia is undertaking a comprehensive reform of its healthcare system to improve the efficiency and accessibility of public healthcare services. A key aspect of this initiative is outsourcing outpatient pharmacy services within the public health sector to retail pharmacies through an electronic prescribing platform known as Wasfaty. The National Unified Procurement Company (NUPCO) manages this platform to ensure spending efficiency and patient accessibility to essential medications. However, there has been a lack of research evaluating the adherence of the Wasfaty e-prescribing platform to established best practices for Computerized Provider Order Entry (CPOE), which are commonly used to assess the performance of various ambulatory e-prescribing systems globally. Objective: This study aimed to assess the level of adherence of Wasfaty to best practices for CPOE. Methods: This descriptive cross-sectional single-center study reviewed filled prescriptions through Wasfaty from May 2022 to December 2023. A list of 60 functional features, including but not limited to patient identification and data access, medication selection, alerts, patient education, data transmission and storage, monitoring and renewals, transparency and accountability, and feedback, was utilized to evaluate adherence. The adherence level was categorized into four groups: fully implemented, partially implemented, not implemented, and not applicable. Two pharmacy interns, a clinical pharmacist, and a researcher, reviewed the prescriptions to determine the platform’s adherence to these 60 CPOE features. Results: From May 2022 to December 2023, a total of 1965 prescriptions were filled in retail pharmacies for out-of-stock medications for 1367 patients. These prescriptions included medications for various areas, with the following distribution: gastroenterology (44.10%), cardiology (18.14%), anti-infectives (2.42%), urology (8.85%), dermatology (3.6%), hematology (0.29%), muscle relaxants (0.8%), neurology (19.17%), pulmonology (1.46%), and other categories (1.23%). Of the 60 functional characteristics a CPOE platform should include, only 19 (31.66%) were fully implemented, while 10 (16.66%) were partially implemented. Conclusions: The Wasfaty platform is deficient in several key functional features necessary for e-prescribing, which are essential for ensuring patient safety and enhancing the satisfaction of both prescribers and patients. This study underscores the importance of improving the Wasfaty platform to reduce the risk of adverse drug events. Full article

Background: Shoulder impingement syndrome (SIS) is a prevalent condition among overhead athletes, often managed through therapeutic exercise interventions. However, the quality of reporting in exercise protocols significantly impacts their reproducibility and clinical implementation. The Consensus for Exercise Reporting Template (CERT) provides a standardized framework to assess the quality of exercise reporting in clinical research. Objectives: This systematic review aimed to evaluate the quality of exercise protocols used to treat SIS in overhead athletes by applying the CERT checklist. Additionally, the risk of bias was assessed to determine the methodological rigor of included studies. Methods: A systematic review was conducted following PRISMA guidelines. Six electronic databases (MEDLINE, CINAHL, Sport Discuss, Web of Science, and Cochrane) were searched for eligible studies. Inclusion criteria encompassed randomized controlled trials (RCTs), cohort studies, and case series that investigated exercise therapy for SIS in overhead athletes. Studies had to be published in English and provide details on exercise interventions. Exclusion criteria included non-human studies, acute injuries, and postoperative management. The primary outcome was the quality of intervention reporting, assessed using the CERT checklist. The secondary outcome was the risk of bias, evaluated using the modified Downs and Black checklist. Results: Five studies met the inclusion criteria, comprising four RCTs and one case series. CERT scores ranged from 6 to 13 (median = 8, IQR = 1), indicating suboptimal reporting quality. Commonly reported CERT items included equipment usage and exercise tailoring. However, key aspects such as adherence, motivation, and intervention fidelity were consistently underreported. None of the included studies provided comprehensive details on exercise interventions as per CERT guidelines, limiting their reproducibility and clinical application. Conclusions: The quality of reporting on exercise-based interventions for SIS in overhead athletes remains insufficient. Critical gaps in adherence monitoring, patient motivation, and intervention fidelity were identified. Future research should prioritize standardized and detailed reporting of exercise interventions to enhance reproducibility and facilitate evidence-based clinical practice. Full article