Bibliographic record
Abstract
Objective. Most biomarker studies of maternal smoking have been based on a single blood or urinary cotinine value, which is inadequate in capturing maternal tobacco exposure over the entire pregnancy. This thesis used maternal hair biomarkers to investigate the association between maternal active and passive smoking, and birthweight for gestational age (BW for GA). Methods. Subjects were 444 term controls drawn from 5,337 participants of a multi-centre nested case-control study of preterm birth in Montreal. Maternal hair, collected after delivery, was measured for average nicotine and cotinine concentration across the pregnancy, assuming hair growth of 1 cm/month. The BW for GA z-score used Canadian population-based standards. Multiple linear regression was used to assess effects on the z-score, after controlling for potential confounders. Results. In regression models for maternal active smoking analysis, the addition of hair nicotine to models containing either self-report or hair cotinine or both self-report and cotinine explained significantly more variance in the BW for GA z-score (p=0.009, p=0.017, and p=0.033, respectively). In maternal passive smoking analysis, no significant effect of ETS on BW for GA was found using hair biomarkers. Conclusion. These results indicate that hair biomarkers are sensitive tools capable of predicting reductions in birthweight for maternal active smoking. The stronger results obtained for nicotine are reflective of the fact that hair nicotine is a better measure of maternal smoking, but it could also suggest that nicotine plays an aetiologic role in affecting foetal growth.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".