Hospitalizations and Mortality in the Lung Health Study
Bibliographic record
Abstract
This report deals with deaths and hospitalizations during the 5-year Lung Health Study, as documented by examination of appropriate records. There were 149 deaths (2.5%) during the study, caused largely by lung cancer and cardiovascular disease, particularly coronary heart disease. A total of 12.8% of participants were hospitalized, with cancer, cardiovascular disease, and nonmalignant respiratory disease accounting for 75% of hospitalizations. There were no significant differences among the original treatment groups for all-cause mortality, lung cancer, or hospitalizations for respiratory disease. Deaths and hospitalizations for cardiovascular disease and coronary artery disease were more common in the smoking intervention plus Atrovent inhaler (SI-A) group, which received ipratropium bromide, than in the smoking intervention plus placebo inhaler (SI-P) group, which received placebo, and the differences approached statistical significance. However, we were unable to find a dose effect, in that differences were not related to self-reported inhaler compliance. In the SI-A group, nine participants were hospitalized for supraventricular tachycardia as compared with two in the SI-P group, and SI-A participants with this condition were unusually compliant with their inhaled medication. When all participants were considered and smoking status considered as a time-dependent covariate, smoking cessation was associated with significant reductions in fatal or nonfatal cardiovascular disease and coronary artery disease.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".