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Record W3091910709 · doi:10.1093/annweh/wxaa077

The Utility of an Occupational Contact Dermatitis Patch Test Database in the Analysis of Workplace Prevention Activities in Toronto, Canada

2020· article· en· W3091910709 on OpenAlexaffabout
D. Linn Holness, Irena Kudla, Joel G. DeKoven, Sandra Skotnicki

Bibliographic record

VenueAnnals of Work Exposures and Health · 2020
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsPublic Health OntarioUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsTest (biology)Occupational safety and healthMedicinePatch testDemographicsEnvironmental healthOccupational medicineHealth careHand eczemaPersonal protective equipmentIntervention (counseling)Family medicineContact dermatitisNursingOccupational exposureAllergy

Abstract

fetched live from OpenAlex

BACKGROUND: Occupational skin diseases are common suggesting that there are still gaps in workplace prevention. Patch test surveillance systems provide an opportunity to collect work related information in addition to clinical information and patch test results. OBJECTIVES: To examine 5 years of data related to workplace prevention by industry sector in a patch test surveillance database for workers with a diagnosis of occupational contact dermatitis. METHODS: The study was approved by the Research Ethics Board of St Michael's Hospital. Information including demographics, clinical history, healthcare utilization, and workplace characteristics and prevention practices in addition to patch test results was collected from consenting patients. RESULTS: Workers in the healthcare and manufacturing sectors were more likely to report workplace training including skin protection training, whereas those in services and construction were less likely to report training. CONCLUSIONS: Collecting basic workplace information with patch test surveillance databases can inform the occupational health and safety system about prevention practices in the workplace and identify areas for focussed intervention.

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 score0.403
Threshold uncertainty score0.445

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 integrity0.0000.000
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 head0.073
GPT teacher head0.368
Teacher spread0.295 · 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

Citations2
Published2020
Admission routes2
Has abstractyes

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