Search Results (47)

Background: Colonoscopy is the gold-standard screening modality for colorectal cancer (CRC) prevention, enabling detection and endoscopic resection of premalignant polyps and reducing CRC incidence and mortality by up to 77% and 53%, respectively. However, colonoscopy effectiveness is substantially dependent on endoscopist expertise, with significant inter-operator variability in adenoma detection rates (ADR) and, consequently, a risk of missed lesions, particularly diminutive and morphologically subtle adenomas. Recent advances in artificial intelligence (AI), specifically computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems utilizing deep learning convolutional neural networks, have emerged as promising technologies to standardize lesion detection accuracy and reduce adenoma miss rates. Methods: A focused narrative literature review was conducted examining randomized controlled trials, meta-analyses, and implementation studies evaluating AI-assisted colonoscopy systems across diverse clinical populations and healthcare settings. Results: Evidence demonstrates that CADe systems consistently improve ADR, particularly for diminutive polyps and morphologically challenging lesions, though superiority over expert endoscopists remains inconsistent. CADx systems reliably meet ASGE-PIVI performance thresholds for diminutive polyp characterization, supporting implementation of resect-and-discard and diagnose-and-leave strategies. However, substantial heterogeneity exists regarding real-world effectiveness, cost-effectiveness, and optimal implementation frameworks across diverse settings. Conclusions: While AI-assisted colonoscopy demonstrates clinical promise in improving lesion detection and enabling optical diagnosis, realizing durable population-level benefit requires the establishment of standardized validation methodologies, large-scale pragmatic trials with patient-centered outcomes, robust regulatory frameworks, and equitable implementation strategies addressing health disparities globally. Full article

This review examines the clinical and practical implications of embedding molecular profiles directly into the 2023 FIGO staging system for endometrial carcinoma, in the context of the 2025 ESGO/ESTRO/ESP guidelines. The primary purpose is to navigate a central conflict in modern oncology: how to deliver increasingly personalized care while maintaining a globally accessible, equitable, and standardized cancer classification system. The 2023 FIGO update represents a paradigm shift from the traditional dualistic model (Type I versus Type II) by allowing molecular findings to redefine stage itself. While this integration offers clear benefits, it introduces significant challenges. First, the system depends on advanced molecular testing, creating a “rich-poor” divide where patients in resource-limited settings are systematically overtreated because testing is unavailable. Second, stage becomes unstable, changing with sequential histologic and molecular re-review, which causes confusion for patients and clinicians. Third, the system lumps prognostically distinct histotypes (Serous, Clear Cell, Carcinosarcoma, and Grade 3 Endometrioid) into a single aggressive stage, obscuring meaningful differences in survival. Fourth, it relies on subjective parameters such as “substantial” lymphovascular space invasion, for which no standardized definition exists, leading to high inter-observer variability. After analyzing these controversies, the review proposes a pragmatic solution: decouple anatomical staging from molecular risk stratification. Staging should remain a purely anatomical, universally applicable descriptor of tumor extent, while molecular and histologic data are used separately within a dynamic risk assessment model, as suggested by the European guidelines. This dual-track approach preserves global comparability, reduces inequity, and maintains diagnostic stability, while still enabling personalized treatment where advanced diagnostics are available. Full article

Background/Objectives: Inter-reader variation can alter unidimensional tumor measurements used in RECIST-oriented response assessment. Unlike systems that automate segmentation or target selection, this study aimed to predict lesion-specific measurement uncertainty itself. Methods: The development cohort comprised 463 lung, liver, and lymph-node lesions from 280 patients in Vol-PACT, with four segmentations per lesion. Inter-reader variability was defined as (maximum longest diameter–minimum longest diameter)/minimum longest diameter. A Swin Transformer was trained to regress this continuous score. A value > 0.20 was used as a pragmatic high-variability alert threshold, with high variability designated as the positive class; this threshold is not equivalent to RECIST progressive disease. Preliminary external evaluation used 21 NSCLC lesions with five reader contours, providing one reader mask at a time. Results: The validation and internal test AUCs were 0.74 (95% CI, 0.61–0.85) and 0.89 (95% CI, 0.80–0.95), respectively. On the internal test set, MAE was 0.10 (95% CI, 0.08–0.12), accuracy was 82% (95% CI, 74–90%), sensitivity 72% (95% CI, 59–85%), and specificity for lower-variability lesions was 92% (95% CI, 82–100%). Across the five external readers, AUCs ranged from 0.77 to 0.92; the wide confidence intervals reflect the small external sample. Conclusions: This proof-of-concept model may provide a measurement-quality signal that supports target selection or adjudication. Larger multi-institutional studies, patient-level resampling, calibration, ablation testing, and workflow validation are required before clinical use. Full article

Background/Objectives. In dementia, progressive changes in spoken language, including reduced lexical diversity, increased disfluency, and syntactic simplification, are well documented. Although computational approaches have demonstrated reliable group differences in individual linguistic features, less attention has been given to the potential coordination between linguistic features and discourse-level moderation of the features with group differences. Methods. Over five domains—syntactic, lexical, disfluency, output, and affective—nine linguistic features were extracted from 552 transcripts (309 dementia, 243 healthy controls) of individuals performing the Cookie Theft picture description task in the DementiaBank Pitt Corpus. The Mann–Whitney U test was used to examine group differences with rank-biserial correlation effect sizes. Fisher z-transformation tests were applied to compare within-group correlation to examine the moderation effect of discourse-pragmatic factors (self-references, hedging, and sentiment polarity) on different groups. Fisher z-transformation tests were used to compare inter-feature correlations between the two groups. All multiple comparisons were controlled using the Benjamini–Hochberg false discovery rate (FDR) procedure. Results. Out of the nine features, word complexity, repetition rate, MLU, and filler rate were significantly different between the group with dementia and healthy controls. Among them, word complexity and repetition rate showed the largest and most consistent effects. Self-referential language showed a stronger association with verbal output in the group with dementia, although the association was not significant when analyses were restricted to one visit per participant. Moreover, one inter-feature relationship (i.e., the positive correlation between short utterance ratio and word complexity) remained significant after FDR correction. This correlation was present only in healthy controls, suggesting that this preserved coordination may be disrupted in dementia. Conclusions. Dementia disrupts not only individual linguistic features but also the relationships between them. The preserved coordination between syntactic simplification and lexical elaboration was found to be absent in speakers with dementia. Inter-feature coordination may, therefore, be able to reveal group differences that individual feature analyses alone cannot detect. Full article

Background: Personalized nutrition, also referred to as precision nutrition, is an emerging approach that integrates genetic, metabolic, phenotypic, behavioral, and environmental characteristics to develop individualized dietary strategies. Obesity, metabolic syndrome (MetS), and metabolic dysfunction-associated steatotic liver disease (MASLD) represent interconnected disorders with substantial inter-individual variability in disease development, metabolic risk, and response to dietary interventions. Although body mass index (BMI) remains widely used for obesity classification, it does not adequately capture differences in body composition, fat distribution, or metabolic health. Consequently, individuals with normal-weight obesity (NWO), characterized by excessive body fat accumulation despite a normal BMI, may remain unidentified despite increased cardiometabolic risk.This narrative review critically evaluates the current evidence on the potential role of personalized nutrition in the prevention and management of obesity, MetS, MASLD, and related cardiometabolic abnormalities. Particular attention is given to five major domains: nutrigenetics, gut microbiota, metabolic phenotyping, body composition assessment, and digital health technologies, with emphasis on their current clinical applicability and limitations. Methods: A structured narrative review was performed using PubMed, Scopus, and Web of Science to identify English-language studies (2003–2026) on personalized nutrition in obesity, normal-weight obesity, metabolic syndrome, and MASLD. Eligible studies were selected according to predefined inclusion and exclusion criteria, and 31 publications were included in the qualitative synthesis. Results: Current evidence suggests that personalized nutrition strategies may contribute to improvements in body weight regulation, insulin sensitivity, lipid metabolism, and liver-related outcomes; however, the magnitude and consistency of these effects remain variable. The integration of genetic, metabolic, microbiome, and phenotypic information may improve individual risk stratification and help identify high-risk groups, including individuals with NWO who may not be recognized through BMI-based assessment alone. Emerging approaches involving multi-omics technologies, microbiome profiling, wearable devices, continuous glucose monitoring, and artificial intelligence-based tools provide promising opportunities for individualized dietary interventions. Nevertheless, limitations related to methodological heterogeneity, insufficient standardization, limited external validation, and the scarcity of long-term pragmatic clinical trials currently restrict their routine implementation. Conclusions: Personalized nutrition represents a promising but still evolving approach for addressing obesity and its metabolic complications, including MetS and MASLD. While the integration of biological, phenotypic, and digital information may support more targeted dietary recommendations, current evidence does not yet fully establish the clinical effectiveness and cost-effectiveness of these approaches in routine care. Future large-scale, longitudinal, and well-designed randomized controlled trials are required to determine which personalized nutrition strategies provide clinically meaningful benefits and for which patient populations. Full article

Background: Anxiety and depression are major contributors to mental-health burden and frequently co-occur in clinical practice. In tele–mental health, routinely captured operational variables such as consultation duration, visit frequency, and follow-up cadence may provide clinical digital phenotypes that complement conventional symptom scales. This study aimed to characterize anxiety–depression comorbidity in a large real-world tele–mental health cohort and to determine whether symptom severity was associated with distinct patterns of healthcare utilization. Methods: We conducted a retrospective real-world study of 3467 patients followed in psychiatry and psychology teleconsultations. Patients were classified as anxiety only, depression only, comorbid anxiety–depression, or neither. Symptom severity was categorized as mild, moderate, or severe using validated questionnaire-based measures; to improve comparability across instruments, scores were additionally harmonized using z-score normalization. Associations between anxiety and depression severity within the comorbid subgroup were examined using a chi-square framework. Telehealth utilization endpoints included consultation duration, number of consultations, and inter-visit interval, analysed overall and stratified by sex, age group, and symptom severity. Results: Anxiety and/or depression were present in 61.7% of the cohort (2140/3467), and anxiety–depression comorbidity accounted for 43.8% of all patients (1520/3467), indicating substantial real-world overlap. Within comorbid cases, anxiety and depression severity were strongly coupled, with depression severity varying systematically across anxiety severity strata (chi-square p = 9.88 × 10⁻¹⁰²). Compared with isolated anxiety or depression, comorbidity was associated with a more intensive healthcare-utilization profile, characterized by a higher mean number of consultations and shorter inter-visit intervals. Among comorbid patients, females showed greater longitudinal service use than males, with more visits and closer follow-up. Resource use also varied according to symptom burden, mainly in depression, supporting a graded relationship between clinical severity and operational care demand. Conclusions: In this large real-world tele–mental health cohort, anxiety–depression comorbidity was highly prevalent, clinically structured, and associated with distinct and measurable resource-use signatures. These findings highlight the novelty and practical value of integrating symptom severity with operational telehealth data to derive pragmatic digital phenotypes of care intensity. Such phenotypes may support risk stratification, triage, follow-up scheduling, and capacity planning in tele–mental health, with potential translational relevance for broader mental healthcare systems. However, these findings should be considered descriptive and hypothesis-generating and warrant further longitudinal validation in other clinical settings. Full article

Background: This study aimed to compare the measurement agreement and intramethod reliability of four cephalometric analysis workflows: manual tracing, semi-automated digital analysis (Dolphin), fully automated AI-based analysis (WebCeph), and expert-adjusted AI analysis (WebCeph+). Methods: In this retrospective method-comparison study, 67 lateral cephalometric radiographs were initially included. After the exclusion of radiographs containing extreme values, 54 radiographs (35 females, 19 males; mean age: 15.0 ± 2.13 years) were analyzed. Twenty-one skeletal, dental, and soft-tissue parameters (13 angular, 8 linear) were evaluated across the four methods. Intramethod repeatability was assessed via the intraclass correlation coefficient (ICC). Intermethod comparisons were analyzed using ANOVA and post hoc pairwise tests. Pragmatic clinical relevance thresholds were predefined as ±2 degrees for angular measurements and ±2 mm for linear measurements. Results: All methods demonstrated high intramethod reliability, with ICC values exceeding 0.90 in 20 out of 21 parameters. Manual and Dolphin methods yielded statistically comparable results (p > 0.05). In contrast, WebCeph differed significantly from manual and/or Dolphin in seven parameters, including SNA, IMPA, Go-Gn length, Pog to N-perpendicular, Wits appraisal, nasolabial angle, and mentolabial angle (p < 0.05). Several discrepancies exceeded the predefined pragmatic thresholds (±2 degrees and ±2 mm), highlighting their potential clinical relevance. After expert adjustment (WebCeph+), statistically significant inter-workflow differences were no longer observed; however, residual individual-level variability remained for selected parameters. Conclusions: Fully automated WebCeph analysis showed limited agreement with manual and semi-automated methods for several clinically relevant measurements. Expert adjustment reduced systematic mean discrepancies and improved agreement with clinician-dependent workflows; however, residual individual-level variability remained for selected parameters. AI-driven cephalometric analysis should therefore be considered a supportive tool requiring specialist verification rather than an unsupervised replacement for conventional methods. Full article

Background: Surveillance of non-muscle-invasive bladder cancer (NMIBC) relies on cystoscopy and urine cytology, both of which have well-recognised limitations. Molecular urine assays have been developed to reduce the burden of invasive surveillance, yet their real-world clinical utility remains uncertain. Uromonitor^® is a quantitative PCR-based assay targeting hotspot variants in the TERT promoter, FGFR3, and KRAS, which are frequently altered in urothelial carcinoma. We evaluated the performance of Uromonitor^® in routine clinical practice and assessed its technical reproducibility. Methods: Uromonitor^® diagnostic test accuracy was retrospectively calculated from samples from patients undergoing investigation for suspected bladder cancer (n = 64) or surveillance (n = 30) following a prior diagnosis at a tertiary referral centre between 2021 and 2023. Uromonitor^® results were compared with histology where available (n = 49, 52%), or with contemporaneous cystoscopy and urine cytology findings (n = 45, 48%). This pragmatic dual reference standard reflects routine clinical practice but may introduce some heterogeneity in diagnostic accuracy verification. A prospective in-house verification cohort was used to assess inter-laboratory reproducibility. Discordant cases underwent orthogonal next-generation sequencing (NGS) analysis. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were calculated for the Uromonitor^® against the standard of care. Results: Ninety-four patients were included in the clinical performance analysis. Overall sensitivity, specificity, PPV, NPV and overall accuracy for Uromonitor^® were 38%, 88%, 63%, 72% and 70%, respectively. Sensitivity was higher in the diagnostic setting (47%; 95% CI 27.3–68.3%) than during surveillance (23%; 95% CI 8.2–50.2%). Several false-negative cases in the verification cohort harboured variants either detectable by NGS at variant allele frequencies below or slightly above the assay’s limit of detection or variants not covered by the assay hotspot design. Inter-laboratory reproducibility was excellent, with 100% concordance observed in the verification cohort. Conclusions: In a real-world clinical setting, Uromonitor^® demonstrated high specificity but limited sensitivity for detection of bladder cancer, particularly during surveillance. A negative result does not reliably exclude recurrence. Assay sensitivity thresholds and restricted variant coverage appear to be key contributors to false-negative results. These findings highlight the need for cautious clinical integration of Uromonitor^®. It is unclear whether this approach has sufficient sensitivity in surveillance to safely reduce cystoscopy frequency. This underscores the need for further refinement of urine-based molecular assays, including a need for enhanced sensitivity and broader mutational coverage before routine clinical adoption. Full article

Marine oil spills pose critical challenges to environmental sustainability and socioeconomic stability. Taking four pivotal cases as the entry point, this study uses comparative case analysis, semi-structured stakeholder interviews, policy analysis and international gap comparison to systematically analyze the evolution of marine oil spill governance in the Taiwan region of China over two decades, aiming to identify systemic gaps and propose actionable reforms. By integrating and explicitly detailing these multiple methodologies, this research not only identifies but also systematically examines the Taiwan region of China’s unique challenges as a non-UN-member entity navigating international conventions like the international convention for the prevention of pollution from ships, 1973, as modified by the protocol of 1978 relating thereto (MARPOL 73/78). Key findings reveal persistent issues in decision-support tools, fragmented inter-agency coordination, and legal inadequacies in compensation mechanisms. The study’s novelty lies in its rigorous synthesis of localized case-driven insights compared with global best practices, proposing a concrete, phased model for a unified task force and context-aware, data-driven contingency plans to enhance real-time response efficiency. It further advocates for pragmatic steps to align the Taiwan region of China’s Marine Pollution Control Act with international standards while critically addressing the transboundary collaboration barriers imposed by its political status, exploring potential pathways through sub-national and regional partnerships. Notably, the 2023 Angel Container case underscores the urgency of modernizing enforcement capacities and integrating advanced technologies. By bridging gaps in governance, legal accountability, and practical international engagement, this research not only advances the Taiwan region of China’s preparedness but also offers a nuanced and adaptable blueprint for coastal regions facing similar geopolitical and environmental constraints. Its recommendations hold significant implications for global marine pollution management, emphasizing the interplay of policy innovation, technological adoption, and pragmatic cross-jurisdictional cooperation. Full article

In this article, we address the issue of the sometimes indeterminate grammatical and functional status of vocatives and vocative-like NPs by proposing a prototype-based approach to their classification. We then explore the socio-pragmatic functions of these vocative types, adopting a variationist perspective that considers both macro- and micro-social factors to determine when the different types of vocatives occur and how they contribute to managing interpersonal relationships. This exploratory analysis is based on data from an online questionnaire featuring Discourse Completion Tasks of over 3000 participants in Germany, Austria, and Switzerland. The findings show that different vocative types fulfill distinct socio-pragmatic functions, ranging from signaling positive politeness to heightening the face-threatening potential of an utterance, depending on the communicative task performed. In addition, their use varies between participants, based on the speakers’ regional background, gender, age, or personality traits. Full article

Platelet-rich plasma (PRP) is widely used in orthopedics and sports medicine as an autologous product; however, substantial heterogeneity in manufacture and incomplete reporting of preparation parameters limits reproducibility and inter-study comparability.: We performed a PRISMA-guided methodological review of studies describing PRP preparation, subsequently focusing on orthopedic applications. MEDLINE/PubMed, Embase, Scopus, the Cochrane Library, Web of Science, and Google Scholar were searched. 7330 records were retrieved; following merging and de-duplication, more than 2500 unique records were screened. The inclusion criteria for our study required studies on PRP that focused on orthopedic use of this preparation and studies that reported a defined methodology for PRP, reported in the abstract or in the manuscript. Extracted variables covered collection and anticoagulation, centrifugation strategy, cellular composition, activation/lysis, processing environment, storage, and time-to-use. Twenty-three orthopedic studies met the inclusion criteria. Whole blood draw volume and anticoagulant were reported in 15/23 studies each; centrifugation parameters (relative centrifugal force/RPM and duration) in 12/23; and PRP phenotype (e.g., leukocyte-poor vs. leukocyte-rich) in 15/23. Platelet metrics (baseline and/or final platelet count/concentration) were reported in 6/23. Sterility/environmental controls were mentioned in 17/23, whereas storage conditions and time-to-use were described in only 3/23. An explicit exogenous activation agent was reported on 1/23. Orthopedic PRP studies frequently omit critical manufacturing and handling descriptors—particularly platelet dose, leukocyte/lymphocyte handling, temperature control, storage/freezing conditions, and time-to-administration—impairing reproducibility and dose comparability. We propose a pragmatic, standardized protocol for preparation of leucocyte/lymphocyte-depleted PRP for orthopedic use (PRP only, without gelification), together with a minimum set of data and parameters to be evaluated. In our opinion these parameters should be included in future studies in order to standardize the production process. The quality of PRP itself could be impacted by such standardization, and the ability to objectively evaluate the results of studies could be enhanced by facilitating the comparison of data emerging from the literature. Full article

Background: Disability progression in multiple sclerosis (MS) is increasingly recognized as a consequence of large-scale brain network disruption rather than isolated regional damage. Although diffusion tensor imaging (DTI) is the reference method for assessing structural connectivity, its limited availability restricts widespread clinical application. There is therefore a critical need for alternative approaches capable of capturing network-level alterations using routinely acquired MRI data. Objective: This study aimed to determine whether synthetic structural connectivity matrices derived from standard regional volumetric MRI can capture clinically meaningful network alterations in MS and predict subsequent functional progression, particularly upper limb decline. Methods: Regional brain volumetry was obtained from routine T1-weighted MRI using an automated, clinically approved volumetric pipeline. Synthetic structural connectivity matrices were generated by integrating principles of structural covariance, distance-dependent connectivity, and disease-specific vulnerability patterns. Graph-theoretical network metrics were extracted to characterize global and regional topology. Machine learning models including logistic regression, support vector machines, random forests, and gradient boosting were trained to predict clinical progression defined by worsening on the 9-Hole Peg Test. Dimensionality reduction was performed using principal component analysis, and model performance was evaluated using balanced accuracy, AUC-ROC, and resampling-based validation. Feature importance analyses were conducted to identify network vulnerability patterns. Results: Synthetic connectivity networks exhibited biologically plausible properties, including preserved but attenuated small-world organization. Global efficiency showed a strong inverse correlation with disability severity (EDSS). Patients with clinical progression demonstrated marked reductions in network integration and segregation, alongside increased characteristic path length. Machine learning models achieved robust prediction of upper limb functional decline, with ensemble-based methods performing best (balanced accuracy > 80%, AUC-ROC up to 0.85). A limited subset of connections accounted for a disproportionate share of predictive power, predominantly involving frontoparietal associative networks, thalamocortical pathways, and inter-hemispheric connections. In a longitudinal subset, network-level alterations preceded measurable clinical deterioration by several months. Conclusions: Synthetic structural connectivity derived from routine volumetric MRI captures clinically relevant network-level disruption in multiple sclerosis and enables accurate prediction of functional progression. By bridging network neuroscience with widely accessible imaging data, this framework provides a pragmatic alternative for connectomic analysis when diffusion imaging is unavailable and supports a network-based understanding of disease evolution in MS. Full article

Credit scoring is the industry-standard methodology for quantifying the creditworthiness and default risk of individual loan applicants. However, assessing the risk at the portfolio level—across different branches or regions—requires more than just aggregating individual scores. This paper presents a simple, pragmatic model for evaluating overall commercial bank portfolio risk by analyzing accumulated credit scores, facilitating effective inter-branch benchmarking. The proposed model is validated using credit score data from two distinct regions of the bank. Logistic regressions by region show that both northern and southern banks maintain low overall risk profiles due to strong portfolio credit scores. However, a nuanced analysis reveals regional discrepancies: the southern region appears riskier when segmented by credit score groupings (indicating a higher concentration of lower-tier borrowers), whereas the northern region exhibits higher risk when analyzed against a broader set of factors, such as approved amounts, maximum potential exposure, and approved versus book rates. This research suggests that portfolio risk is not one-dimensional; effective risk management requires analyzing both individual scores and the interaction of loan characteristics, particularly when comparing regional performance. Full article

With the understanding that language variation, whether synchronic or diachronic, is, for the most part, not random but systematic and predictable, linguists and philologists have long engaged with the task of providing accounts and explanations for how a phenomenon in a given language moves from stage a to stage b, with a temporally preceding b. A usage-based approach to diachronic change takes as its basis a fundamental link between usage and the evolution of linguistic structure through time. More specifically, motivations for language change are sought in areas that involve speakers using language and interacting with one another, such as discourse and communication, as well as cognitive processes involved in producing and understanding language. Mechanisms that contribute to language change include pragmatic inferences, frequency, (inter)subjectification and discourse management (turn-taking). Full article

Objectives: To develop a simplified MRI-based shorthand assessment method, referred to as the Sagittal Tibial Epi-Physis (STEP) Shorthand, for skeletal age assessment in skeletally immature patients with anterior cruciate ligament (ACL) injuries. This study aimed to elaborate a single-plane MRI-based skeletal age estimation tool and to explore its feasibility and inter-rater reliability in comparison with existing MRI-based shorthands. Methods: This prospective study included 130 knee MRIs (79% males) from 97 skeletally immature patients (overall average age of 14.0 ± 2.1 years) with ACL injuries treated between February 2022 and January 2025. A new shorthand assessment method was developed based on sagittal T1-weighted MRI evaluation of the proximal tibial epiphysis. A validation cohort of 74 MRIs was independently evaluated by four raters with different levels of expertise using the STEP, Meza, and Politzer shorthand atlases. Inter-rater reliability (ICC), intra-rater agreement (Cohen’s kappa), and association with chronological age (Spearman rho) were calculated. Results: The STEP Shorthand tool demonstrated a strong association with chronological age (rho = 0.890, p < 0.001) with consistent associations across sex subgroups. Inter-rater reliability was high and comparable to established MRI-based shorthands. The use of a focused sagittal T1-weighted evaluation allowed for a simplified and reproducible assessment across raters with varying experience levels. Conclusions: The STEP Shorthand represents a pragmatic and reliable tool for MRI-based skeletal age assessment in pediatric and adolescent patients with ACL injuries. The STEP Shorthand can support timely decision-making in surgical planning and enhance standardization across different levels of clinical expertise. Full article