An International Pooled Analysis for Obtaining a Benchmark Dose for Environmental Lead Exposure in Children
Bibliographic record
Abstract
Lead is a recognized neurotoxicant, but estimating effects at the lowest measurable levels is difficult. An international pooled analysis of data from seven cohort studies reported an inverse and supra-linear relationship between blood lead concentrations and IQ scores in children. The lack of a clear threshold presents a challenge to the identification of an acceptable level of exposure. The benchmark dose (BMD) is defined as the dose that leads to a specific known loss. As an alternative to elusive thresholds, the BMD is being used increasingly by regulatory authorities. Using the pooled data, this article presents BMD results and applies different statistical techniques in the analysis of multistudy data. The calculations showed only a limited variation between studies in the steepness of the dose-response functions. BMD results were quite robust to modeling assumptions with the best fitting models yielding lower confidence limits (BMDLs) of about 0.1-1.0 μ g/dL for the dose leading to a loss of one IQ point. We conclude that current allowable blood lead concentrations need to be lowered and further prevention efforts are needed to protect children from lead toxicity.
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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 | 0.073 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.015 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".