Effets de la pollution de l’air sur la santé: Une approche pour conseiller les patients à l’aide de la cote air santé
Bibliographic record
Abstract
OBJECTIVE: To inform family physicians about the health effects of air pollution and to provide an approach to counseling vulnerable patients in order to reduce exposure. SOURCES OF INFORMATION: MEDLINE was searched using terms relevant to air pollution and its adverse effects. We reviewed English-language articles published from January 2008 to December 2009. Most studies provided level II evidence. MAIN MESSAGE: Outdoor air pollution causes substantial morbidity and mortality in Canada. It can affect both the respiratory system (exacerbating asthma and chronic obstructive pulmonary disease) and the cardiovascular system (triggering arrhythmias, cardiac failure, and stroke). The Air Quality Health Index (AQHI) is a new communication tool developed by Health Canada and Environment Canada that indicates the level of health risk from air pollution on a scale of 1 to 10. The AQHI is widely reported in the media, and the tool might be of use to family physicians in counseling high-risk patients (such as those with asthma, chronic obstructive pulmonary disease, or cardiac failure) to reduce exposure to outdoor air pollution. CONCLUSION: Family physicians can use the AQHI and its health messages to teach patients with asthma and other high-risk patients how to reduce health risks from air pollution.
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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 | 0.023 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".