Prenatal Exposure to PM2.5 Components and Preterm Birth
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to study maternal exposure to PM2.5 components and preterm birth. METHODS: Counts of hospitalizations due to preterm birth at the Fourth Hospital of Shijiazhuang City were collected from 2014 to 2019. The daily number of hospitalizations and short-term exposure to five types of PM2.5 components were examined using a time-stratified case-crossover method. The overall mixture of PM2.5 components and its relationship with preterm birth were analyzed via Bayesian kernel regression. RESULTS: In the single-pollutant analyses, maternal exposed to components of PM2.5 had an increased risks per interquartile increase in concentration during lag 0-2 days. The mixed component analysis revealed exposure to a mixture of PM2.5 components increased the risk of preterm birth. BCs are the main factors affecting the overall effects. CONCLUSIONS: PM2.5 components may have a potential influence of preterm birth.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".