Risk factors for falling among community-dwelling seniors using home-care services: An extended hazards model with time-dependent covariates and multiple events
Bibliographic record
Abstract
The identification of risk factors for falls in longitudinal studies becomes difficult because of exposures that change during the follow-up and also because individual subjects may experience an event more than once. These issues have been neglected and improper statistical techniques have been used. The typical approaches have been to report the proportion of fallers or the time to first fall. Both avoid the underlying assumption of independence between events and discard pertinent data. We review the existing methods and propose a Cox hazards extension. We exemplify it in the study of potential risk factors associated with all falls in 959 seniors. Finally, we compare the results of the proposed Wei, Lin, & Weissfeld (WLW) method with those of several other techniques. Stable exposure variables measured at baseline and updated time-varying exposures include socio-demographic characteristics, BMI, nutritional risk, alcohol consumption, home hazards, gait and balance, and medications. Results demonstrate that the usual methods of analyzing risk factors for falling are inappropriate, as they produce considerable biases relative to the WLW model using time-dependent covariates. Results also show that modeling for first events may be inefficient, given that the risk of occurrence varies between falls.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".