Genital malformations in newborns of female nickel-refinery workers
Bibliographic record
Abstract
OBJECTIVES: This study investigated whether pregnant women employed in nickel-exposed work areas are at elevated risk of delivering a newborn with a genital malformation. METHODS: In this register-based cohort study, data about pregnancy outcome and occupation were obtained using the Kola Birth Registry. Each record in the Registry was assigned a categorical nickel exposure rating according to the occupation the delivering woman had at the time of becoming pregnant, using, as guidelines, the water-soluble nickel subfraction of the inhalable aerosol fraction obtained by personal monitoring for nickel-refinery workers or the measured urinary nickel concentrations. The reference population comprised delivering women from Moncegorsk with a background exposure level. The association of the outcome with the assigned exposure ratings was analyzed in a logistic regression model, adjusted for parity, maternal malformation, exposure to solvents, and infection in early pregnancy. RESULTS: The odds ratio for nickel-exposed women delivering a newborn with a genital malformation was 0.81 [95% confidence interval (95% CI) 0.52-1.26], and that for an undescended testicle was 0.76 (95% CI 0.40-1.47). CONCLUSIONS: In this study no negative effect of maternal exposure to water-soluble nickel was found on the risk of delivering a newborn with malformations of the genital organs. The results should be interpreted with caution since there were few cases in the higher exposure groups. The findings do not exclude the possibility of an effect on the risk of other congenital malformations and adverse outcomes (including reduced fertility).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".