Selecting Patients for Falls-Prevention Protocols: An Evidence-Based Approach on a Geriatric Rehabilitation Unit
Bibliographic record
Abstract
Geriatric rehabilitation treatment focuses on maximizing functional independence in older adults to facilitate a return to independent living following hospitalization. Rehabilitation professionals must therefore balance the need to foster increasing activity levels among patients while, at the same time, preventing falls and potential injuries. This study investigated measures of patient cognition and aspects of health as predictors of the risk for falls among geriatric rehabilitation patients. Fall rates and patient data were collected over an 18-month period. Data from 98 patients were included in the data set. The number of falls was regressed on the patient data to investigate their predictive power. Analyses were also conducted comparing fallers and nonfallers across the independent variables. Results revealed that the primary diagnosis was the only factor evidencing sufficient power for empirical identification of patients at the greatest risk for falls. The clinical implications of findings, in terms of an evidence-based approach to managing falls risk in this population, are discussed in this article.
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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 | 0.067 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".