Validation of a Falls Risk Screening Tool Derived From InterRAI Acute Care Assessment
Bibliographic record
Abstract
OBJECTIVES: This study aimed to develop and validate a falls risk screening tool derived from interRAI Acute Care (AC) Assessment. METHODS: For derivation and validation, two prospective cohorts were recruited from AC hospitals in Australia. The derivation cohort comprised 1418 patients from 11 hospitals. In the validation cohort, 393 patients were recruited from four hospitals. The interRAI AC tool was used to collect comprehensive geriatric assessment data at admission. In-hospital falls were documented from medical records. A falls risk score was calculated using logistic regression. Predictive ability was compared with St. Thomas Risk Assessment Tool In Falling elderlY (STRATIFY), using area under curve (AUC). The validation cohort provided external validity. RESULTS: Complete data in the derivation cohort were available for 1288 patients (91%), with 75 (5.8%) having an in-hospital fall. The derived interRAI AC falls risk score (range = 0-6) had significantly better predictive ability (AUC = 0.70, 95% confidence interval [CI] = 0.63-0.76) compared with St. Thomas Risk Assessment Tool In Falling elderlY (AUC = 0.64, 95% CI = 0.58-0.70) (P = 0.033). At a cut point of three, 54 of 75 falls were correctly predicted by the falls risk score derived from interRAI AC (sensitivity = 0.72 [95% CI = 0.60-0.82] and specificity = 0.60 [95% CI = 0.57-0.62]). The falls risk score performed similarly in the validation cohort. CONCLUSIONS: The falls risk tool developed from interRAI AC is a valid measure to screen for in-hospital falls. Reduction in assessment burden without loss of fidelity can be achieved through integrating the risk screener within the interRAI hospital system, which automatically triggers protocols for falls prevention based on identified risk.
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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 | 0.018 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".