EXTREME AIR POLLUTION EVENTS FROM BUSHFIRES AND CARDIO-RESPIRATORY HOSPITAL ADMISSIONS IN SYDNEY, AUSTRALIA 1994-2007
Bibliographic record
Abstract
Background and aims: Extreme air pollution events due to bushfire smoke are expected to increase as a consequence of climate change, yet little has been published about their population health impacts. We examined the association between bushfire smoke pollution events and hospital admissions in Sydney from 1997-2004. Methods: Events were defined as days for which smoke from bushfires caused the 24 hour city-wide average concentration of PM10 to exceed the 99th percentile. We used a time-stratified case-crossover design with conditional logistic regression modeling adjusted for daily meteorology, flu epidemics and holidays. Odds ratios (OR) and 95% confidence intervals (CI) for admissions on event compared with non-event days were estimated. We assessed admissions for all cardiovascular conditions, ischaemic heart diseases, hypertensive diseases, cerebrovascular diseases, all respiratory conditions, asthma, chronic obstructive pulmonary disease (COPD), bronchitis and pneumonia. Results: There were 52 days during the study period in which the extreme particulate pollution was attributable to bushfire smoke. On the day of the smoke events all respiratory hospital admissions increased by 7% (OR 1.071, 95%CI, 1.033,1.112). Admissions for COPD increased 16% (OR 1.168, 95%CI 1.080, 1.263) and asthma by 14% (OR 1.145, 95%CI 1.036, 1.265). Results were similar at a lag of one day. No associations were observed with other respiratory diagnoses or with cardiovascular admissions. Conclusions: Bushfire smoke pollution events were associated with increases in admissions for respiratory, rather than cardiovascular conditions.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".