MétaCan
Menu
Back to cohort
Record W1544067322 · doi:10.1002/ajim.22354

Prostate cancer and occupational exposure to whole‐body vibration in a national population‐based cohort study

2014· article· en· W1544067322 on OpenAlexaff
Marcella K. Jones, Marianne Harris, Paul A. Peters, Michael Tjepkema, Paul A. Demers

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicEffects of Vibration on Health
Canadian institutionsToronto Metropolitan UniversityOccupational Cancer Research CentreStatistics CanadaPublic Health OntarioUniversity of Toronto
FundersHealth Research Board
KeywordsMedicineProstate cancerCohort studyCancerCohortHazard ratioWhole body vibrationProportional hazards modelPopulationProstateOncologyInternal medicineDemographyEnvironmental healthConfidence interval

Abstract

fetched live from OpenAlex

BACKGROUND: Following preliminary evidence from observational studies, we test the potential relationship between whole-body vibration (WBV) and prostate cancer in a cohort study. METHODS: WBV exposure was assigned based on occupation in 1991 and 1,107,700 participants were followed for incident prostate cancer until the end of 2003. Adjusted hazard rate ratios (HRs) were calculated using Cox proportional hazards modeling. RESULTS: 17,922 incident prostate cancer cases were observed. WBV-exposed men in Natural and Applied Sciences Occupations had a 37% elevated risk of prostate cancer (95% CI 1.09-1.72) and WBV-exposed men in Trades, Transport, and Equipment Operators Occupations had a 9% reduced risk (95% CI 0.86-0.97). Independent of WBV exposure, small but significant differences in risk were seen for several occupational categories. CONCLUSIONS: We found no consistent relationship between WBV and prostate cancer. Further research could focus on other exposures or specific occupations in the studied categories to determine what may be contributing to the observed differences in prostate cancer risk.

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)0.003
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 score0.029
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.033
GPT teacher head0.370
Teacher spread0.337 · 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

Citations14
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Industrial MedicineSame topicEffects of Vibration on HealthFrench-language works237,207