Development of a predictive model for loss of functional and cognitive abilities in long-term care home residents: a protocol
Bibliographic record
Abstract
INTRODUCTION: Long-term care (LTC) residents require extensive assistance with daily activities due to physical and cognitive impairments. Medical treatment for LTC residents, when not aligned with residents' wishes, can cause discomfort without providing substantial benefits. Predictive models can equip providers with tools to guide treatment recommendations that support person-centred medical decision-making. This study protocol describes the derivation and validation of time-to-event predictive models for (1) permanent loss of independence in physical function, (2) permanent severe cognitive impairment and (3) time alive with complete dependence for those with disability starting from the date of onset. METHODS AND ANALYSIS: We will use population-based administrative health data from the Institute for Clinical Evaluative Sciences of all LTC residents in Ontario, Canada, to construct the derivation and internal validation cohorts. The external validation cohort will use data from LTC residents in Alberta, Canada. Predictors were identified based on existing literature, patient advisors and expert opinions (clinical and analytical). We identified 50 variables to predict the loss of independence in physical function, 58 variables to predict the loss of independence in cognitive function and 36 variables to predict the time spent in a state of dependence. We will use time-to-event models to predict the time to loss of independence and time spent in the state of disability. Full and reduced models (using a step-down procedure) will be developed for each outcome. Predictive performance will be assessed in both derivation and validation cohorts using overall measures of predictive accuracy, discrimination and calibration. We will create risk groups to present model risk estimates to users as median time-to-event. Risk groups will be externally validated within the Alberta LTC cohort. ETHICS AND DISSEMINATION: Ethics approval was obtained through the Bruyère Research Institute Ethics Committee. Study findings will be submitted for publication and disseminated at conferences. The predictive algorithm will be available to the general public.
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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 | 0.037 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".