Self-assessed Physical Health Predicts 10-Year Mortality After Myocardial Infarction
Bibliographic record
Abstract
PURPOSE: In spite of their widespread use in other fields, global measures of health are not commonly used in determining the prognosis of patients with myocardial infarction (MI). The objective of the present study was to ascertain the relationship between self-assessed physical health at the time of the MI and long-term mortality. METHODS: This was a prospective cohort study of 284 patients with MI admitted to an academic community hospital between July 1995 and December 1996 who completed the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36). The physical component scale from the SF-36 was used as a self-assessment of physical health. All-cause mortality was assessed 10 years later by using the Social Security Death Index. RESULTS: Patients with lower self-reported physical health were significantly more likely to be women; older; depressed; have a history of coronary artery disease; have a family history of MI; have a non-Q wave MI; have a Killip class 3 or 4 MI; have hypertension, diabetes mellitus, renal insufficiency, and chronic obstructive pulmonary disease; and have a longer hospitalization period. Patients with higher physical component scores had significantly lower mortality in the 10 years after MI and this persisted after adjusting for confounders (hazard ratio = 0.97 [95% CI 0.96-0.99], P = .001). CONCLUSIONS: These data suggest that self-assessed physical health provides information on the long-term prognosis of patients with MI above and beyond that provided by traditional risk predictors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".