Predicting First Time Falls: Validating a Novel Algorithm in Long Term Care
Bibliographic record
Abstract
To determine the predictive validity of the 1stFall algorithm in long-term care (LTC) residents across four Canadian provinces. This retrospective cohort study included all clients admitted to LTC between 2006-2017 with no history of falls in the past 30 days. The outcome was occurrence of a fall and logistic regression analysis was performed to assess predictive validity. A total of 199,997 LTC residents were studied (71% were >80 years old, 66% women, and 17% had severe cognitive impairment). For the total sample, clients in the 2nd, 3rd, 4th and 5th risk categories had 1.15, 1.58, 2.66, and 3.76 times greater odds of falling than the 1st category, respectively. Similar trends were observed across provinces. 1stFall was developed to predict the risk of a first-time fall event in individuals with no history of a recent fall. 1stFall identified LTC residents at risk of a first-time fall, supporting its use in routine care.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".