A comparison of 3 frailty measures and adverse outcomes in the intake home care population: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: In Ontario, Canada, nearly all home care patients are assessed with a brief clinical assessment (interRAI Contact Assessment [interRAI CA]) on admission. Our objective was to compare 3 frailty measures that can be operationalized using the interRAI CA. METHODS: We conducted a retrospective cohort study using linked patient-level assessment and administrative data for all Ontario adult (≥ 18 yr) home care patients assessed with the interRAI CA in 2014. We employed multivariable logistic models to compare the Changes in Health, End-stage disease and Signs and Symptoms Scale for the Contact Assessment (CHESS-CA), Assessment Urgency Algorithm (AUA) and the Frailty Index for the Contact Assessment (FI-CA) that was created for this study. Our outcomes of interest were death, hospital admission and emergency department visits within 90 days, and assessor-rated need for comprehensive geriatric assessment (CGA). RESULTS: In 2014, there were 228 679 unique adult home care patients in Ontario assessed with the interRAI CA. Controlling for age, sex and health region, being in a higher frailty level defined by any measure increased the likelihood of experiencing adverse outcomes. Among all assessments, CHESS-CA was best suited for predicting death and hospital admission, and either AUA or FI-CA for predicting perceived need for CGA. Previous emergency department visits were more predictive of future visits than frailty. Model fit was independent of whether the assessment was completed over the phone or in person. INTERPRETATION: Frailty measures from the interRAI CA identified patients at higher risk for death, hospital admission and perceived need for CGA. In jurisdictions where the CHESS-CA and AUA are already built into the electronic home care platform, such as Ontario, patients identified as high risk should be prioritized for proactive referral and care planning, and may benefit from greater involvement of primary care and other health professionals in the circle of 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".