SELF-RATED HEATH PREDICTS INCIDENT MODERATE/SEVERE DISABILITY IN AGING MEN: THE MANITOBA FOLLOW-UP STUDY
Bibliographic record
Abstract
Abstract Background There is evidence that Self-Rated Health (SRH) predicts death, but less evidence that SRH predicts new disability. Objective: To determine if SRH predicts moderate/severe disability in a prospective cohort study of aging men. Methods We conducted an analysis of a prospective cohort study of men who qualified for air crew training in the Royal Canadian Air Force, which began in 1948. Since 2004, an annual survey is sent out which measures age-reference SRH, functional status, and the Short-Form-36 (SF-36). In 2004, 796 men were alive, responded to the questionnaire and did not have disability. Functional status has been measured with an annual questionnaire which includes items on Basic Activities of Daily Living (BADLs) and Instrumental ADLs (IADLs). We considered impairment to be two or more BADLs impaired (of five considered) or three or more impaired IADLs (of seven). We calculated the median time to disability, and compared groups with a log rank test. We then constructed a proportional hazards model for the outcome of new disability. Results SRH was associated with incident disability: Compared to those with Excellent SRH, the Hazard Ratio (95% confidence interval) was 1.79 (1.19, 2.68) for those with Very Good SRH; 2.87 (1.91, 4.30) for those with Good SRH; and 2.40 (2.04, 5.65) for those with Fair/Poor/Bad SRH. This association persisted after adjusting for age, the SF-36 and chronic diseases. Conclusions: SRH predicts new disability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".