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Record W2299764915 · doi:10.1136/thoraxjnl-2015-207994

Obstructive sleep apnoea and frequency of occupational injury

2016· letter· en· W2299764915 on OpenAlexafffund
A. J. Hirsch Allen, Julie E. Park, Patrick Daniele, John A. Fleetham, C. Frank Ryan, Najib Ayas

Bibliographic record

VenueThorax · 2016
Typeletter
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia HospitalProvidence Health Care Research InstituteUniversity of British Columbia
FundersCanadian Institutes of Health ResearchVancouver Coastal Health Research InstituteBritish Columbia Lung Association
KeywordsMedicineConfoundingPolysomnographyInternal medicinePhysical therapyPoison controlVigilance (psychology)PediatricsEmergency medicineApnea

Abstract

fetched live from OpenAlex

We sought to determine whether patients with obstructive sleep apnoea (OSA) are at increased risk of occupational injury (OI). Patients referred to the University of British Columbia Hospital Sleep Laboratory for suspected OSA (May 2003 to July 2011 were recruited and rates and types of validated OI (that caused at least 1 day of disability) in the 5 years prior to polysomnography were calculated. In a sample of 1236, patients with OSA were twice as likely (OR=1.93, 95% CI 1.06 to 3.50, p=0.03) to suffer at least one OI compared with patients without OSA. This association was attenuated (OR=1.76, CI 0.86 to 3.59, p=0.12) after controlling for confounders. In a secondary analysis, patients with OSA were almost three times more likely (OR=2.88, CI 1.02 to 8.08, p=0.05) to suffer from an injury more likely related to reduced vigilance (eg, a fall or commercial motor vehicle crash) when compared with patients without OSA, and this again was attenuated after controlling for confounders (OR=2.42, CI 0.085 to 6.93, p=0.10).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.338
Teacher spread0.306 · 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 source (direct Gemma or distilled Codex), 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

Citations26
Published2016
Admission routes2
Has abstractyes

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