Associations Between Toxic Metal Exposure and Childhood Nephrotic Syndrome
Bibliographic record
Abstract
Background: Acute mercury toxicity can cause nephrotic syndrome in children and adults. It is unknown if chronic exposure to toxic metals is a risk factor for childhood nephrotic syndrome. Our aims were to evaluate the association of toxic metal exposure and longitudinal outcomes among children with nephrotic syndrome. Methods: We analyzed data from INSIGHT, a prospective childhood nephrotic syndrome cohort. We included children (1-18 years) with nephrotic syndrome diagnosed from 2001-2019 from the Greater Toronto Area, Canada. Toenail clippings were collected and tested for total mercury, copper, arsenic, selenium, cadmium, tin, and lead concentrations. We evaluated associations between individual and mixed metal concentrations and relapse rate, steroid resistance, frequent relapses, and steroid-sparing medication use. Results: Nails were analyzed from 298 children with nephrotic syndrome. Median age at diagnosis was 4 years (IQR 3-6), 183 (63%) were male, 104 (36%) were South Asian, and 17 (6%) were steroid resistant. Less than 1% had elevated nail mercury and 10% had other elevated metal concentrations. None had metal concentrations in a potentially toxic range. There were no significant associations between nail mercury concentration and nephrotic syndrome relapses (RR 1.05, 95%CI 0.96-1.14), steroid resistance (OR 0.90, 95%CI 0.59-1.32), frequent relapses or steroid dependence (OR 1.01, 95%CI 0.83-1.24), or steroid-sparing medication use (OR 1.12, 95%CI 0.92-1.35). Concentrations of other individual or combined metals were also not associated with outcomes. Conclusion: High exposure to mercury and other toxic heavy metals is rare among children with nephrotic syndrome. Findings suggest that chronic exposure to toxic heavy metals is not associated with nephrotic syndrome outcomes.Figure. Correlation between nail mercury concentration and relapse rate
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".