Lack of Association between Fingernail Selenium and Thyroid Cancer Risk: A Case-Control Study in French Polynesia
Bibliographic record
Abstract
BACKGROUND: Numerous studies have suggested that selenium deficiency may be associated with an increased risk for several types of cancer, but few have focused on thyroid cancer. MATERIALS AND METHODS: We examined the association between post-diagnostic fingernail selenium levels and differentiated thyroid cancer risk in a French Polynesian matched case-control study. Conditional logistic regression models were used to estimate odds ratios and 95% confidence intervals. RESULTS: The median selenium concentration among controls was 0.76 μg/g. Significantly, we found no association between fingernail selenium levels and thyroid cancer risk after conditioning on year of birth and sex and additionally adjusting for date of birth (highest versus lowest quartile: odds-ratio=1.12, 95% confidence interval: 0.66-1.90; p-trend=0.30). After additional adjustment for other covariates, this association remained non-significant (p-trend=0.60). When restricting the analysis to thyroid cancer of 10 mm or more, selenium in nails was non-significantly positively linked to thyroid cancer risk (p-trend=0.09). Although no significant interaction was evidenced between iodine in nails and selenium in nails effect (p=0.70), a non-significant (p-trend =0.10) positive association between selenium and thyroid cancer risk was seen in patients with less than 3 ppm of iodine in nails. The highest fingernail selenium concentration in French Polynesia was in the Marquises Islands (M=0.87 μg/g) and in the Tuamotu-Gambier Archipelago (M=0.86 μg/g). CONCLUSIONS: Our results do not support, among individuals with sufficient levels of selenium, that greater long-term exposure to selenium may reduce thyroid cancer risk. Because these findings are based on post-diagnostic measures, studies with prediagnostic selenium are needed for corroboration.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".