Air Pollution and Hospitalization for Headache in Chile
Bibliographic record
Abstract
The authors performed a time-series analysis to test the association between air pollution and daily numbers of hospitalizations for headache in 7 Chilean urban centers during the period 2001-2005. Results were adjusted for day of the week and humidex. Three categories of headache-migraine, headache with cause specified, and headache not otherwise specified-were all associated with air pollution. Relative risks for migraine associated with interquartile-range increases in specific air pollutants were as follows: 1.11 (95% confidence interval (CI): 1.06, 1.17) for a 1.15-ppm increase in carbon monoxide; 1.11 (95% CI: 1.06, 1.17) for a 28.97-microg/m(3) increase in nitrogen dioxide; 1.10 (95% CI: 1.04, 1.17) for a 6.20-ppb increase in sulfur dioxide; 1.17 (95% CI: 1.08, 1.26) for a 69.51-ppb increase in ozone; 1.11 (95% CI: 1.00, 1.19) for a 21.51-microg/m(3) increase in particulate matter less than 2.5 mum in aerodynamic diameter (PM(2.5)); and 1.10 (95% CI: 1.04, 1.15) for a 37.79-microg/m(3) increase in particulate matter less than 10 mum in aerodynamic diameter (PM(10)). There was no significant effect modification by age, sex, or season. The authors conclude that air pollution appears to increase the risk of headache in Santiago Province. If the relation is causal, the morbidity associated with headache should be considered when estimating the burden of illness and costs associated with poor air quality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".