Reply: Maternal lead exposure, secondary sex ratio and dose-exposure fallacy
Bibliographic record
Abstract
Sir, We appreciate the comments and suggestions of Dr Jongbloet who points out that the increase in the sex ratio among children of those mothers in the third quintile of blood lead measurements in our data (Jarrell et al., 2006) may represent changes predicted by the overripeness ovopathy concept (Jongbloet, 2004). We had noted this increased sex ratio, but had concluded, in light of the data with respect to our other biomarkers of lead, that overall there did not appear to be any consistent association between any of the lead biomarkers and the sex ratio. We have undertaken a secondary analysis to determine if there is evidence of an increase in the sex ratio over the first three quintiles (n = 507) when using the blood lead measurements as a continuous variable. In this re-analysis, we found a significant increase in the adjusted odds ratio (OR) for male birth per unit (μg/dl) increase in blood lead [OR: 1.14; 95% confidence interval (CI): 1.02–1.28; P = 0.03]. There was not a significant change in the sex ratio from the third to fifth quintiles (n = 476). For each unit increase in maternal blood lead over the last three quintiles the OR for a male was 0.98 (95% CI: 0.93–1.03, P = 0.43).
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 | 0.009 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.020 | 0.035 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".