Contribution of Self-Reported Health Ratings to Predicting Frailty, Institutionalization, and Death Over a 5-Year Period
Bibliographic record
Abstract
Cross-sectional data from Phase 1 of the Canadian Study of Health and Aging was used to examine the relationship between two self-report health measures: "How would you say your health is these days?"(HEALTH) and "How much do your health troubles stand in the way of your doing the things you want to do?"(TROUBLE). The contribution of these measures to predictive models for institutionalization and mortality is examined, using linked data from Phases 1 and 2. Their relationship to a proposed frailty measure is also examined. At CSHA-1, a majority of respondents perceived that they were in good health and did not feel that their health problems interfered with their preferred activities. At all frailty levels, a majority of both males and females rated their health as "very good" or "pretty good." As frailty increased, health problems increasingly interfered with normal activities. Logistic regression of the longitudinal data indicated that, despite their correlation, HEALTH and TROUBLE cannot act as proxies for each other. They appear to predict independently; adding one to the other significantly improved prediction of institutionalization and mortality.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".