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Record W3198676992 · doi:10.1080/22423982.2021.1972525

Serum selenium levels and asthma among seafood processing workers in Greenland

2021· article· en· W3198676992 on OpenAlexaboutno aff
Birgitte Hamann Laustsen, Øyvind Omland, Else Toft Würtz, Louise Helskov Jørgensen, Jakob Hjort Bønløkke

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2021
Typearticle
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsnot available
Fundersnot available
KeywordsAsthmaSeleniumEnvironmental healthMedicineGeographyInternal medicineChemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.320
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

Explore more

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