Correction: A Low Selenium Level Is Associated with Lung and Laryngeal Cancers
Bibliographic record
Abstract
Purpose: It has been suggested that selenium deficiency is a risk factor for several cancer types.We conducted a casecontrol study in Szczecin, a region of northwestern Poland, on 95 cases of lung cancer, 113 cases of laryngeal cancer and corresponding healthy controls. Methods:We measured the serum level of selenium and established genotypes for four variants in four selenoprotein genes (GPX1, GPX4, TXNRD2 and SEP15).Selenium levels in the cases were measured after diagnosis but before treatment.We calculated the odds of being diagnosed with lung or laryngeal cancer, conditional on selenium level and genotype.Results: Among lung cancer cases, the mean selenium level was 63.2 mg/l, compared to a mean level of 74.6 mg/l for their matched controls (p,0.0001).Among laryngeal cancer cases, the mean selenium level was 64.8 mg/l, compared to a mean level of 77.1 mg/l for their matched controls (p,0.0001).Compared to a serum selenium value below 60 mg/l, a selenium level above 80 mg/l was associated with an odds ratio of 0.10 (95% CI 0.03 to 0.34; p = 0.0002) for lung cancer and 0.23 (95% CI 0. 09 to 0.56; p = 0.001) for laryngeal cancer.In analysis of four selenoprotein genes we found a modest evidence of association of genetic variant in GPX1 with the risk of lung and laryngeal cancers. Conclusion:A selenium level below 60 mg/l is associated with a high risk of both lung and laryngeal cancer.
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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 | 0.004 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.093 | 0.019 |
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".