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An Official American Thoracic Society Workshop Report: Presentations and Discussion of the Sixth Jack Pepys Workshop on Asthma in the Workplace

2017· article· en· W2753136487 on OpenAlexafffund
Susan M. Tarlo, Jean‐Luc Malo, F. de Blay, Nicole Le Moual, Paul K. Henneberger, Dick Heederik, Monika Raulf, Christopher Carlsten, André Cartier

Bibliographic record

VenueAnnals of the American Thoracic Society · 2017
Typearticle
Languageen
FieldMedicine
TopicOccupational exposure and asthma
Canadian institutionsToronto Western Hospital
FundersNational Institutes of HealthWorkplace Safety and Insurance BoardAmerican Thoracic Society
KeywordsTheme (computing)MedicineSection (typography)PopulationEnvironmental healthAdvertising

Abstract

fetched live from OpenAlex

The Sixth Jack Pepys Workshop on Asthma in the Workplace focused on six key themes regarding the recognition and assessment of work-related asthma and airway diseases: (1) cleaning agents and disinfectants (including in swimming pools) as irritants and sensitizers: how to evaluate types of bronchial reactions and reduce risks; (2) population-based studies of occupational obstructive diseases: use of databanks, advantages and pitfalls, what strategies to deal with biases and confounding?; (3) damp environments, dilapidated buildings, recycling processes, and molds, an increasing problem: mechanisms, how to assess causality and diagnosis; (4) diagnosis of occupational asthma and rhinitis: how useful are recombinant allergens (component-resolved diagnosis), metabolomics, and other new tests?; (5) how does exposure to gas, dust, and fumes enhance sensitization and asthma?; and (6) how to determine probability of occupational causality in chronic obstructive pulmonary disease: epidemiological and clinical, confirmation, and compensation aspects. A summary of the presentations and discussion is provided in this proceedings document. Increased knowledge has been gained in each topic over the past few years, but there remain aspects of controversy and uncertainty requiring further 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 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 score0.499
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.082
GPT teacher head0.442
Teacher spread0.359 · 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

Citations25
Published2017
Admission routes2
Has abstractyes

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