Serum selenium levels and asthma among seafood processing workers in Greenland
Bibliographic record
Abstract
Selenium levels in the Inuit population of Greenland have been declining during the last decades. The association between Selenium and asthma has been investigated previously, but with conflicting results. The objective was to measure human serum Se (s-Se) in Greenlandic seafood processing workers, to compare with levels recorded in previous decades and to establish if s-Se is associated with asthma or lung function. Data, including questionnaire answers, spirometry, skin-prick test and s-Se from 324 seafood processing workers in Greenland were collected during 2016–2017. Mean s-Se values were compared by t-test and one-way ANOVA. Associations between s-Se and asthma, symptoms from the lower airways at work and lung function were assessed using linear regression. The mean s-Se was 96.2 µg/L. S-Se was higher among non-smokers and workers living in settlements. Workers with asthma did not have s-Se levels significantly different from those of non-asthmatics. We found a positive association between s-Se levels and FEV1 values. Selenium levels appear to continue declining in Greenland, presumably because of a more Westernised lifestyle. The health effects of declining Selenium levels remain unclear. We did not establish an association between s-Se and asthma, but we did record a positive association between s-Se and FEV1.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".