Predicting First Time Falls: Validating a Novel Algorithm in Long Term Care
Bibliographic record
Abstract
To determine the predictive validity of the 1<sup>st</sup>Fall 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 2<sup>nd</sup>, 3<sup>rd</sup>, 4<sup>th</sup> and 5<sup>th</sup> risk categories had 1.15, 1.58, 2.66, and 3.76 times greater odds of falling than the 1<sup>st</sup> category, respectively. Similar trends were observed across provinces. 1<sup>st</sup>Fall was developed to predict the risk of a first-time fall event in individuals with no history of a recent fall. 1<sup>st</sup>Fall 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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; both teacher heads agree on what is shown here.
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".