Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to present a technique for estimating the effect of ambient air pollution mix on health outcomes. MATERIAL AND METHODS: We created a technique of indexing air pollution mix as a cause of the increased odds of health problems. As an illustrative example, we analyzed the impact of pollution on the frequency of emergency department (ED) visits due to colitis among young patients (age < 15 years, N = 11 110). Our technique involves 2 steps. First, we considered 6 ambient air pollutants (carbon monoxide, nitrogen dioxide, sulphur dioxide, ozone, and 2 measures of particulate matter) treating each pollutant as a single exposure. Odds ratios (ORs) for ED visits associated with a standard increase (interquartile range - IQR) in the pollutants levels were calculated using the case-crossover technique. The ORs and their 95% confidence intervals (95% CIs) were also found for lagged exposures (for lags 1-9 days). Second, we defined a Health Air Study Index (HASI) to represent the combined impact of the 6 air pollutants. RESULTS: We obtained positive and statistically significant results for individual air pollutants and among them the following estimations: OR = 1.06 (95% CI: 1.02-1.1, NO2 lag 3, IQR = 12.8 ppb), OR = 1.04 (95% CI: 1.01-1.07, SO2 lag 4, IQR = 2.3 ppb), OR = 1.04 (95% CI: 1-1.06, PM lag 3, IQR = 6.2 μg/m3). Among the re-calculated ORs with the HASI values as an exposure, the highest estimated value was OR = 1.37 (95% CI: 1.12-1.68, for 1 unit of the HASI, lag 3). CONCLUSIONS: The proposed index (HASI) allows to confirm the pattern of associations for lags obtained for individual air pollutants. In the presented example the used index (HASI) indicates the strongest relation with the exposure lagged by 3 days.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".