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Record W2098771053 · doi:10.5271/sjweh.682

Healthy worker effect in cohort studies on chronic bronchitis

2002· review· en· W2098771053 on OpenAlexaff
Katja Radon, Marcel Goldberg, Margaret R. Becklake

Bibliographic record

VenueScandinavian Journal of Work Environment & Health · 2002
Typereview
Languageen
FieldMedicine
TopicOccupational exposure and asthma
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsChronic bronchitisMedicineCohortCohort studyBronchitisOdds ratioConfidence intervalPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: Despite the recognition of selection biases arising from the healthy worker effect in occupational mortality studies, the possibility of similar effects in occupational cohort studies on respiratory symptoms is not well known. Two mechanisms are responsible for the healthy worker effect in respiratory cohort studies. One is health-based selection of workers into employment (healthy him effect), and the other is health-based differential losses to follow-up (healthy worker survivor effect). The aim of the present paper was to estimate the magnitude of the healthy worker survivor effect in cohort studies of symptoms of chronic bronchitis. METHODS: A meta-analysis of occupational cohort studies of symptoms of chronic bronchitis was performed that included published articles identified in searches of the Medline bibliographic databases between 1980 and March 2001 and the reference lists of the located articles. RESULTS: Eight cohort studies were identified using an a priori selection criterion. The pooled odds ratio of the prevalence of chronic bronchitis for subjects leaving the cohorts was 1.23 when these subjects were compared with those who remained under study (95% confidence interval 1.04-1A4). CONCLUSIONS: The prevalence of respiratory symptoms among exposed workers may he underestimated if the healthy worker survivor effect is not taken into account

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.963
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.045
GPT teacher head0.367
Teacher spread0.322 · 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.

Study designOther design
Domainnot available
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

Citations60
Published2002
Admission routes1
Has abstractyes

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