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Record W2773576253 · doi:10.1097/jom.0000000000001197

Medical Monitoring for Occupational Asthma Among Toluene Diisocyanate Production Workers in the United States

2017· article· en· W2773576253 on OpenAlexaff
Laura D. Cassidy, Brent Doney, Meilin Wang, Laura Kurth, Patrick R. Conner, James J. Collins, Michael Carson, Don Molenaar, Carrie A. Redlich, Eileen Storey

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicOccupational exposure and asthma
Canadian institutionsStan Cassidy Foundation
FundersNational Institute for Occupational Safety and HealthNational Institutes of HealthDow Chemical Company
KeywordsToluene diisocyanateOccupational asthmaMedicineProtocol (science)Medical surveillanceAsthmaOccupational safety and healthData collectionEnvironmental healthMedical emergencyHealth surveillanceOccupational exposureMultidisciplinary approachPathologyAlternative medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to describe a study of medical monitoring methods and lessons learned in detecting health outcomes in U.S. plants producing toluene diisocyanate (TDI). METHODS: A multidisciplinary team implemented a medical and environmental monitoring program in three TDI plants. RESULTS: Of 269 eligible workers, 197 (73%) participated and 42 (21%) met symptom and/or lung function criteria that would trigger evaluation for possible asthma over 5 years of data collection. Subsequent evaluation was delayed for most, and a web-based data collection system improved timeliness. CONCLUSION: Medical monitoring of TDI workers identified workers triggering further assessment per study protocol. Systems and/or personnel to ensure rapid follow-up are needed to highlight when triggering events represent potential cases of asthma needing further evaluation. Implementation of a research protocol requires resources and oversight beyond an occupational health program.

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)0.001
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 score0.008
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.034
GPT teacher head0.329
Teacher spread0.294 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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