A benchmark concentration analysis for manganese in drinking water and IQ deficits in children
Bibliographic record
Abstract
BACKGROUND: Manganese is an essential nutrient, but in excess, can be a potent neurotoxicant. We previously reported findings from two cross-sectional studies on children, showing that higher concentrations of manganese in drinking water were associated with deficits in IQ scores. Despite the common occurrence of this neurotoxic metal, its concentration in drinking water is rarely regulated. OBJECTIVE: We aimed to apply a benchmark concentration analysis to estimate water manganese levels associated with pre-defined levels of cognitive impairment in children, i.e. drop of 1%, 2% and 5% in Performance IQ scores. METHODS: Data from two studies conducted in Canada were pooled resulting in a sample of 630 children (ages 5.9-13.7 years) with data on tap water manganese concentration and cognition, as well as confounders. We used the Bayesian Benchmark Dose Analysis System to compute weight-averaged median estimates for the benchmark concentration (BMC) of manganese in water and the lower bound of the credible interval (BMCL), based on seven different exposure-response models. RESULTS: The BMC for manganese in drinking water associated with a decrease of 1% Performance IQ score was 133 μg/L (BMCL, 78 μg/L); for a decrease of 2%, this concentration was 266 μg/L (BMCL, 156 μg/L) and for a decrease of 5% it was 676 μg/L (BMCL, 406 μg/L). In sex-stratified analyses, the manganese concentrations associated with a decrease of 1%, 2% and 5% Performance IQ in boys were 185, 375 and 935 μg/L (BMCLs, 75, 153 and 386 μg/L) and 78, 95, 192 μg/L (BMCLs, 9, 21 and 74 μg/L) for girls. CONCLUSION: Studies suggest that a maximum acceptable concentration for manganese in drinking water should be set to protect children, the most vulnerable population, from manganese neurotoxicity. The present risk analysis can guide decision-makers responsible for developing these standards.
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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".