External validation of approaches to prediction of falls during hospital rehabilitation stays and development of a new simpler tool
Bibliographic record
Abstract
OBJECTIVES: To test the external validity of 4 approaches to fall prediction in a rehabilitation setting (Predict_FIRST, Ontario Modified STRATIFY (OMS), physiotherapists' judgement of fall risk (PT_Risk), and falls in the past year (Past_Falls)), and to develop and test the validity of a simpler tool for fall prediction in rehabilitation (Predict_CM2). PARTICIPANTS: A total of 300 consecutively-admitted rehabilitation inpatients. METHODS: Prospective inception cohort study. Falls during the rehabilitation stay were monitored. Potential predictors were extracted from medical records. RESULTS: Forty-one patients (14%) fell during their rehabilitation stay. The external validity, area under the receiver operating characteristic curve (AUC), for predicting future fallers was: 0.71 (95% confidence interval (95% CI): 0.61-0.81) for OMS (Total_Score); 0.66 (95% CI: 0.57-0.74) for Predict_FIRST; 0.65 (95% CI 0.57-0.73) for PT_Risk; and 0.52 for Past_Falls (95% CI: 0.46-0.60). A simple 3-item tool (Predict_CM2) was developed from the most predictive individual items (impaired mobility/transfer ability, impaired cognition, and male sex). The accuracy of Predict_CM2 was 0.73 (95% CI: 0.66-0.81), comparable to OMS (Total_Score) (p = 0.52), significantly better than Predict_FIRST (p = 0.04), and Past_Falls (p < 0.001), and approaching significantly better than PT_Risk (p = 0.09). CONCLUSION: Predict_CM2 is a simpler screening tool with similar accuracy for predicting fallers in rehabilitation to OMS (Total_Score) and better accuracy than Predict_FIRST or Past_Falls. External validation of Predict_CM2 is required.
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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 | 0.048 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".