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Record W2050252436 · doi:10.1136/oem.59.10.655

Risk of bladder cancer in foundry workers: a meta-analysis

2002· review· en· W2050252436 on OpenAlexafffund
Reimar R. W. Gaertner, Gilles Thériault

Bibliographic record

VenueOccupational and Environmental Medicine · 2002
Typereview
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsMcGill UniversityWorkplace Safety & Insurance Board
FundersWorkplace Safety and Insurance BoardMcGill University
KeywordsMedicineConfoundingBladder cancerEnvironmental healthMeta-analysisEpidemiologyLung cancerRelative riskCancerDemographyOncologyInternal medicineConfidence interval

Abstract

fetched live from OpenAlex

To clarify the inconsistent reports of bladder cancer risk in foundry workers, a meta-analytic review of epidemiological studies was undertaken. Summary risk estimates (SRE) were calculated from 40 systematically extracted results. Weakly increased risks were observed overall, with an SRE of 1.11. Twenty three selected study results with better exposure information yielded an SRE of 1.16. This weak increase in risk is consistent with estimates obtained from dose-response trends of PAH exposures in aluminium smelter workers. Summary estimates did not vary substantially with exposure quality, study design, control for smoking, or when limiting the meta-analysis to large study results. Exposure-response findings showed significantly increased risks of about 1.6 to 1.7 after 20 or more years of employment, but this was based on few studies. Occupation specific SREs showed a 40-50% increased risk among moulders, casters, and unskilled foundry labourers. There was limited evidence that bladder cancer risk correlated with lung cancer risk, which is a more established risk among foundry workers. The small increased risk observed is prone to bias and confounding. Further studies of dose-response trends would greatly aid in determining whether this observed association is causal.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.416
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.000

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 head0.092
GPT teacher head0.352
Teacher spread0.260 · 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 designMeta-analysis
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

Citations46
Published2002
Admission routes2
Has abstractyes

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