Fine particulate matter exposure and renal function: A population-based study among pregnant women in China
Bibliographic record
Abstract
Fine particulate matter (PM2.5) is the most serious environmental threat worldwide. The nephrotoxicity of PM2.5 has been demonstrated in older adults, but no study has addressed the impacts of PM2.5 exposure on renal function in pregnant women, who are recognized to be vulnerable and susceptible to PM2.5 exposure. To evaluate whether exposures to PM2.5 total mass and its chemical constituents were associated with reduced renal function among pregnant women in China. We measured serum concentrations of urea nitrogen (UN), uric acid (UA) and creatinine for 10,052 pregnant women in Shanghai, China. Exposures to PM2.5 total mass and its 5 key chemical constituents during the whole pregnancy and each trimester of pregnancy was represented by satellite-based models. Exposures to PM2.5 total mass and its chemical constituents of organic matter (OM), black carbon (BC), nitrate (NO3−) and ammonium (NH4+) were positively associated with serum levels of UN and UA, and negatively associated with estimated glomerular filtration rate (eGFR). An interquartile rang (IQR) increase in PM2.5 total mass, OM, BC, NO3– and NH4+ exposure in third trimester was associated with 1.33 (β = -1.33, 95% CI, −1.79, −0.87), 1.67 (β = -1.67, 95% CI, −2.26, −1.07), 1.29 (β = -1.29, 95% CI,-1.89, −0.70), 1.16 (β = -1.16, 95% CI,-1.66, −0.65) and 0.76 (β = -0.76, 95% CI, −1.08, −0.44) mL/min/1.73 m2 decrease in eGFR, respectively. We concluded that exposures to PM2.5 during pregnancy were associated with decreased renal function among pregnant women.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".