Time Trends in Mortality Associated with Depression: Findings from the Stirling County Study
Bibliographic record
Abstract
OBJECTIVE: to address the question of whether a mortality risk associated with depression in a 1952 representative sample of Stirling County adults changed in a new sample in 1970, and whether there was a change in associations with cigarette smoking and alcoholism. METHOD: sample members were interviewed about depression and cigarette smoking. General physicians were interviewed by psychiatrists regarding alcoholism. Information about death as of December 31, 1992, was provided by Statistics Canada. Proportional hazards models were fitted in the 2 samples to assess the mortality risks associated with depression among men and women during 20 years of follow-up, and additionally among men with heavy smoking and alcoholism. Specific causes of death were investigated. RESULTS: hazard ratios representing the association between depression and premature death among men were 2.6 (95% CI 1.4 to 4.9) and 2.8 (95% CI 1.5 to 5.1), respectively, in the 1952 and 1970 samples for the first 10 years of follow-up. Hazard ratios for women were 1.4 (95% CI 0.6 to 3.2) and 1.2 (95% CI 0.5 to 2.9). The risk associated with depression among men was independent of alcoholism and heavy smoking. Depression and alcoholism were significantly associated with death by external causes and circulatory disease; heavy smoking was significantly associated with malignant neoplasms. CONCLUSION: the mortality associated with depression did not change during the period from 1952 to 1970. Depressed men experienced a significant mortality risk that was not matched among depressed women and also was not due to alcoholism and heavy smoking.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".