Smokers' use of acute care hospitals--a prospective study.
Bibliographic record
Abstract
BACKGROUND: Previous Canadian estimates of hospital use by smoking history have been derived by applying disease-specific "smoking-attributable fractions" to administrative data. For this analysis, health survey data were linked to hospitalization data at an individual level, permitting prospective measures of hospital use by smoking status and age. DATA AND METHODS: Data for 28,255 respondents (outside Quebec) to the 2000/2001 Canadian Community Health Survey (CCHS) were linked to the Hospital Person-Oriented Information Database. Days in hospital over four years were quantified for each respondent and examined in relation to smoking status in 2000/2001. Multiple logistic regression was used to examine the association between smoking and hospitalization, while controlling for confounders. RESULTS: During the four years after their CCHS interview, current daily smokers and former daily smokers who had quit in the past five years averaged more than twice as many days in hospital as did never-daily smokers. Altogether, excess hospital days for current and former smokers aged 45 to 74 numbered 7.1 million over four years, and accounted for 32% of all hospital days used by people in this age group.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".