Through the Eye: Retinal Changes of Prenatal Mercury Exposure in Grassy Narrows First Nation, Canada
Bibliographic record
Abstract
Since the 1960s, Grassy Narrows First Nation, Canada, has been exposed to methylmercury (MeHg) from fish consumption following Hg discharge from a chloralkali plant. Prenatal exposure to MeHg is known to affect the neurodevelopment of fetuses and the retina is sensitive to neurodevelopmental damage. The multidisciplinary, cross-sectional Niibin study, developed with Grassy Narrows First Nations, included visual examinations with retinal evaluation using optical coherence tomography (OCT). The present analyses focused on the 59 participants (116 eyes) with umbilical cord Hg measurements, sampled between 1971 and 1992. Associations between cord blood Hg and retinal thickness layers surrounding the optic nerve head (RNFL) and inner macula (GC-IPL) were examined using mixed-effect models. Higher cord blood Hg was significantly associated with reduced thickness of GC-IPL layers across all macular sectors; less pronounced associations were observed for RNFL. A qualitative clinical assessment of the OCT results showed that persons with cord blood Hg concentrations ≥ 5.8 µg/L were more likely to present bilateral abnormal retinal thinning (OR = 3.51; [95% CI: 1.06-11.53]). These findings suggest that, in this Indigenous community, prenatal MeHg exposure may have enduring effects on retinal thickness and underline the importance of OCT technology in providing tailored eye care.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".