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Record W1577719428 · doi:10.1002/ajim.22280

Recent trends in published occupational cancer epidemiology research: Results from a comprehensive review of the literature

2013· review· en· W1577719428 on OpenAlexaff
P.M. Raj, Karin Hohenadel, Paul A. Demers, Shelia Hoar Zahm, Aaron Blair

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2013
Typereview
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsPublic Health OntarioUniversity of TorontoOccupational Cancer Research Centre
Fundersnot available
KeywordsEpidemiologyMedicineOccupational cancerEnvironmental healthOccupational exposureOccupational medicineEnvironmental epidemiologyOccupational safety and healthEpidemiology of cancerGerontologyCancerDemographyPathologyBreast cancerInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess trends in occupational cancer epidemiology research through a literature review of occupational health and epidemiology journals. METHODS: Fifteen journals were reviewed from 1991 to 2009, and characteristics of articles that assessed the risk of cancer associated with an occupation, industry, or occupational exposure, were incorporated into a database. RESULTS: The number of occupational cancer epidemiology articles published annually declined in recent years (2003 onwards) in the journals reviewed. The number of articles presenting dose-response analyses increased over the review period, from 29% in the first 4 years of review to 49% in the last 4 years. CONCLUSION: There has been a decrease in the number of occupational cancer epidemiology articles published annually during the review period. The results of these articles help determine the carcinogenicity of workplace exposures and permissible exposure limits, both of which may be hindered with a decline in research.

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)0.008
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0310.036
Science and technology studies0.0000.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.001
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 head0.380
GPT teacher head0.490
Teacher spread0.110 · 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.

Study designSystematic review
DomainEvaluation
GenreReview

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

Citations13
Published2013
Admission routes1
Has abstractyes

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