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Record W41280559 · doi:10.1186/1897-4287-10-s3-a7

Selenium and the risk of cancer of the lung and larynx. A case-control study from a region with low selenium

2012· article· en· W41280559 on OpenAlexaff
Katrzyna Jaworska, Satish Gupta, Katarzyna Durda, Magdalena Muszyńska, Grzegorz Sukiennicki, Elżbieta Jaworowska, Tomasz Grodzki, Mieczysław Sulikowski, Piotr Woloszczyk, Janusz Wójcik, Jakub Lubiński, Cezary Cybulski, Tadeusz Dębniak, Marcin Lener, Steven A. Narod, Ping Sun, Jan Lubiński, Anna Jakubowska

Bibliographic record

VenueHereditary Cancer in Clinical Practice · 2012
Typearticle
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsWomen's College Hospital
Fundersnot available
KeywordsMedicineLung cancerSeleniumCase-control studyOdds ratioSelenoproteinInternal medicineGPX1CancerOncologyGastroenterologyGlutathione peroxidaseOxidative stress

Abstract

fetched live from OpenAlex

Selenium deficiency has been suggested by several studies to be associated with cancer risk. We conducted a case-control study in Szczecin, a region of northwestern Poland, on 86 cases of lung cancer, 87 cases of laryngeal cancer and an equal number of healthy controls. We studied the serum level of selenium and genotypes for four variants in four selenoprotein genes (GPX1, GPX4, TXNRD2 and SEP15) and the odds of being diagnosed with lung or laryngeal cancer. Among lung cancer cases, the mean selenium level was 63.2 µg/l, compared to a mean level of 74.7 µg/l for their matched controls (p 80 µg/l) was associated with an odds ratio of 0.10 (95% CI 0.03 to 0.34; p = 0.0002) for lung cancer and 0.24 (95% CI 0.10 to 0.59; p = 0.002) for laryngeal cancer. In four selenoproteins studied here we found a modest associations of genetic variants in GPX1 and GPX4 with lung and TXNRD2 with laryngeal cancer risk. In this region of endemic low selenium level, there is a strong inverse association between the level of serum selenium and the risks of lung and laryngeal cancer.

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)0.004
metaresearch head score (Gemma)0.004
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 score0.054
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.036
GPT teacher head0.369
Teacher spread0.334 · 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
Published2012
Admission routes1
Has abstractyes

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