www.mdpi.com/journal/ijerph Extricating Sex and Gender in Air Pollution Research: A Community-Based Study on Cardinal Symptoms of Exposure
Bibliographic record
Abstract
Abstract: This study investigated sex and gender differences in cardinal symptoms of exposure to a mixture of ambient pollutants. A cross sectional population-based study design was utilized in Sarnia, ON, Canada. Stratified random sampling in census tracts of residents aged 18 and over recruited 804 respondents. Respondents completed a community health survey of chronic disease, general health, and socioeconomic indicators. Residential concentrations of NO2, SO2, benzene, toluene, ethylbenzene and o/m/p-xylene were estimated by land use regression on data collected through environmental monitoring. Classification and Regression Tree (CART) analysis was used to identify variables that interacted with sex and cardinal symptoms of exposure, and a series of logistic regression models were built to predict the reporting of five or more cardinal symptoms (5+ CS). Without controlling for confounders, higher pollution ranks increased the odds ratio (OR) of reporting 5+ CS by 28 % (p < 0.01; Confidence Interval (CI): 1.07–1.54). Females were 1.52 (p < 0.05; CI: 1.03–2.26) times more likely more likely to report 5+ CS after controlling for income, age and chronic diseases. The CART analysis showed that allergies and occupational exposure classified the sample into the most homogenous groups of
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".