A Systematic Review of Post-Fall Care: Developing an Integrated Continuum from Medical Assessment to Functional Rehabilitation
Bibliographic record
Abstract
Background: Falls in older adults are a frequent global health occurrence, a sign of physiologic failure, and lead to injury, functional decline, fear of falling, and death. In spite of prevention measures, postfall care is often fragmented, focusing on the management of injury and neglect of etiology. Aim: This review aggregates current evidence to propose a standardized, patient-centered plan for comprehensive post-fall care aimed at addressing gaps in care from the time of initial response through long-term restoration of function. Methods: A narrative review of recent literature (2025) was conducted to summarize and synthesize evidence on the components of an optimal post-fall pathway, including initial assessment, interdisciplinary study, and rehabilitation strategies. Results: The results present an uninterrupted, five-step process: 1) Initial nurse response and triage; 2) Complete medical workup to determine etiology; 3) Interdisciplinary care planning; 4) Restoration of function through physical and occupational therapy; and 5) Secondary prevention and transition planning. This model emphasizes the key roles of all health care professionals in creating a unitary care continuum. Conclusion: This integrated, multidisciplinary practice can transform a fall from an incident to an opportunity for holistic assessment, proactive prevention of subsequent risk, preservation of patient autonomy, and improvement in quality of life.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".