Long-term mercury exposure and cognitive functions in a First Nation community in Northern Ontario, Canada
Bibliographic record
Abstract
Prenatal, childhood, and current mercury (Hg) exposure through fish consumption have each been associated with cognitive deficits, but little information exists on the consequences of long-term exposure among adults. Since 1962, Grassy Narrows First Nation has been exposed to Hg from an industrial discharge. Average Hair Hg (HHg) concentrations, initially very high, decreased over time and stabilized in the 1990's. Montreal Cognitive Assessment (MoCA) test outcomes were analyzed in 85 persons aged 32-75 y (median: 53 y) with respect to retrospective year-based HHg measurements between 1970 and 1997 and current blood Hg. Since the MoCA has not been clinically validated for Indigenous populations, residuals of age- and education-adjusted scores were used (MoCA-r scores). Lower MoCA-r scores were observed among persons in the higher quartile of maximum HHg compared to those in the lower quartile (p = 0.007). Clustering of the test items yielded 3 clusters representing verbal fluency and abstraction, cognitive flexibility and working memory, and visuospatial functioning. To model the evolution of HHg over time, longitudinal mixed effect models (LMEM) were performed with persons with ≥ 10 repeated year-based HHg measurements. Higher long-term past HHg was associated with lower MoCA-r and all cluster scores. No association was observed between MoCA-r or cluster scores and blood Hg, which reflects recent exposure. The findings suggest that legacy exposure can affect cognitive functioning decades later, even when average current concentrations have decreased to below recommended guidelines. Prospective studies could provide information on the rate of decline and the possible future impact of current exposure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".