Mortality and hospital admission rates for unintentional nonfire-related carbon monoxide poisoning across Canada: a trend analysis
Bibliographic record
Abstract
BACKGROUND: The epidemiology of mortality and morbidity from carbon monoxide poisoning in Canada has received little attention. Our objective was to evaluate trends in mortality and hospital admission rates for unintentional nonfire-related carbon monoxide poisoning across Canada. METHODS: Age- and sex-standardized mortality (1981-2009) and hospital admission (1995-2010) rates by age group, sex and site of carbon monoxide exposure were calculated for each province and for all of Canada. We quantified the long-term trends by calculating the average annual percent change. Multivariable Poisson regression was used to estimate incidence rate ratios (IRRs) of carbon monoxide poisoning across age groups, sex and month of occurrence. RESULTS: In Canada, there were 1808 unintentional nonfire-related carbon monoxide poisoning deaths between 1981 and 2009 and 1984 admissions to hospital between 1995 and 2010. Average annual decreases of 3.46% (95% confidence interval [CI] -4.59% to -2.31%) and 5.83% (95% CI -7.79% to -3.83%) were observed for mortality and hospital admission rates, respectively. Mortality (IRR 5.31, 95% CI 4.57 to 6.17) and hospital admission (IRR 2.77, 95% CI 2.51 to 3.03) rates were elevated in males compared with females. Decreased trends in the rates were observed for all sites of carbon monoxide exposure, but the magnitude of this decrease was lowest in residential environments. Deaths and admissions to hospital were most frequent from September to April, with peaks in December and January. INTERPRETATION: Mortality and hospital admission rates for unintentional nonfire-related carbon monoxide poisoning in Canada have declined steadily. Continued efforts should focus on reducing carbon monoxide poisoning during the cooler months and in residential environments.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".