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Record W2321921098 · doi:10.1177/154193120004402709

A Case-Control Study of Medication use and Acute Occupational Injury

2000· article· en· W2321921098 on OpenAlexaff
Ben‐Tzion Karsh, Francisco B. P. Moro, Michael J. Smith, Bridget C. Booske, François Sainfort

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2000
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsMedicineOdds ratioOccupational injuryOccupational safety and healthWorkers' compensationTest (biology)Emergency medicineCase-control studyAcute injuryInjury preventionPhysical therapyInternal medicinePoison controlCompensation (psychology)SurgeryPsychologyPathology

Abstract

fetched live from OpenAlex

The purpose of this study was to test the hypothesis that workers who use medications that cause drowsiness are at increased risk of having an acute occupational injury. To test the hypothesis, a case-control study (n=1223 cases, n=1202 controls) was conducted where the sampling frame was composed of employees who had Worker's Compensation claims in one Midwestern state between March and October of 1997. Cases were employees whose cause of injury was acute (i.e. caught in, struck by, or fall). Controls, on the other hand, were employees whose cause of injury was not acute (i.e. strain injuries). The results of the study supported the hypothesis by showing that the use of drowsing medications significantly increased the risk of having an acute occupational injury (odds ratio=2.45, 95% CI = 1.00–6.01), after adjusting for 12 other risk factors. Age modified the effect such that only younger workers who took drowsing medication were at increased risk of acute occupational injuries.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.057
GPT teacher head0.382
Teacher spread0.325 · 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

Citations0
Published2000
Admission routes1
Has abstractyes

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