Maternal Exposure to Drinking-water Chlorination By-products and Small-for-gestational-age Neonates
Bibliographic record
Abstract
BACKGROUND: There is concern about possible effects of disinfection by-products on reproductive outcomes. The purpose of this study was to evaluate the association between maternal exposure to chlorination by-products and the risk of delivering a small for-gestational-age (SGA) neonate. METHODS: We conducted a population-based case-control study in the Québec City (Canada) area. Term newborn cases with birth weights <10th percentile (n = 571) were compared with 1925 term controls with birth weights ≥10th percentile. Concentrations of trihalomethanes and haloacetic acids in the water-distribution systems of participants were monitored during the study period, and a phone interview on maternal habits was completed within 3 months after childbirth. We estimated chlorination by-products ingestion during the last trimester of pregnancy and trihalomethanes doses resulting from inhalation and dermal exposure. We evaluated associations between chlorination by-products in utero exposure and SGA by means of unconditional logistic regression with control of potential confounders. RESULTS: When total trihalomethanes and the 5 regulated haloacetic acids concentrations were divided into quartiles, no clear dose-response relationship was found with SGA. However, increased risk was observed when haloacetic concentrations were above the fourth quartile and when either trihalomethanes or haloacetic acids concentrations were above current water standards (adjusted OR= 1.5 [95% confidence interval = 1.1-1.9] and 1.4 [1.1-1.9], respectively). Inhalation and dermal absorption of trihalomethanes did not contribute to this risk, but a monotonic dose-response was found with haloacetic acids ingestion. CONCLUSION: Oral exposure to high levels of chlorination by-products in drinking water could be a risk factor for term SGA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".