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Record W2114981635 · doi:10.1002/ajim.10208

Validation of expert assessment of occupational exposures

2003· article· en· W2114981635 on OpenAlexaff
Lin Fritschi, Louise Nadon, Geza Benke, Ramzan Lakhani, Benoit Latreille, Marie‐Élise Parent, Jack Siemiatycki

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2003
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsArmand Frappier MuseumInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsChecklistMedicineOccupational hygieneOccupational exposureOccupational medicineEnvironmental healthExposure assessmentOccupational safety and healthPsychologyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Assessment by experts may be the best method available for retrospective occupational exposure measurement in community-based studies. This study was undertaken to examine the validity of occupational exposure assessment by comparing the ratings of experienced raters with previously recorded industrial hygiene measurements. METHODS: We obtained 50 measurements from industrial hygiene records, covering a variety of jobs and substances and created 47 job descriptions around these measurements. Three raters were asked to assess exposure to a checklist of 19 substances (including those substances which had been measured). We estimated the sensitivity of the raters in correctly detecting those substances known to have been present. RESULTS: Using a liberal criterion for the ratings, the average sensitivity among the raters was 90%. Using a more stringent criterion, the average sensitivity was 73%. Among substances coded as present, the raters were quite accurate in rating the relative concentration and frequency of exposure. CONCLUSIONS: This trial demonstrated that a team of experienced raters could successfully characterize jobs in which important exposures occurred.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.042
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.060
GPT teacher head0.365
Teacher spread0.305 · 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

Citations81
Published2003
Admission routes1
Has abstractyes

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