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Record W1969903220 · doi:10.1097/jom.0b013e3181f7cb4c

Antioxidants and Pulmonary Function Among Police Officers

2010· article· en· W1969903220 on OpenAlexaff
Luenda E. Charles, Cecil M. Burchfiel, Anna Mnatsakanova, Desta Fekedulegn, Cathy Tinney-Zara, P Joseph, Holger J. Schünemann, John M. Violanti, Michael E. Andrew, Heather M. Ochs‐Balcom

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2010
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsMcMaster University
FundersNational Institute for Occupational Safety and Health
KeywordsMedicinePulmonary function testingCalorieVitaminInternal medicineVitamin EAnalysis of variancePhysiologyVitamin CAntioxidantEnvironmental healthEndocrinologyDemographyBiochemistryBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine associations of dietary antioxidant intake and pulmonary function. METHODS: Antioxidant data (vitamins A, C, D, E, magnesium, and omega-3 fatty acids) were abstracted from food frequency questionnaires. Pulmonary function was measured using American Thoracic Society criteria. We used analysis of variance to investigate associations. RESULTS: Among 79 police officers (57% male), forced vital capacity was positively and significantly associated with vitamin A after adjustment for age, gender, height, race, smoking status, and pack-years of smoking, and with magnesium after adjustment for those risk factors plus total calories, all supplement use, and abdominal height. Among current/former smokers only, mean levels of all pulmonary function measures were significantly associated with vitamin E; smoking status significantly modified these relationships. CONCLUSIONS: Increased intake of vitamin A, vitamin E (among current/former smokers only), and magnesium was associated with better pulmonary function.

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 score0.007
Threshold uncertainty score0.855

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)0.0010.000

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 head0.032
GPT teacher head0.373
Teacher spread0.341 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

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