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Record W2779799388 · doi:10.1136/oemed-2017-104783

Reanalysis of non-occupational exposure to asbestos and the risk of pleural mesothelioma

2017· letter· en· W2779799388 on OpenAlexaff
Murray M. Finkelstein

Bibliographic record

VenueOccupational and Environmental Medicine · 2017
Typeletter
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsAsbestosMesotheliomaChrysotileMedicineOccupational exposureEnvironmental healthPathology

Abstract

fetched live from OpenAlex

Marsh and colleagues1 have published a review and meta-analysis of non-occupational exposure to asbestos and the risk of pleural mesothelioma. They confirmed that non-occupational exposures to asbestos fibres are associated with a large increased risk of pleural mesothelioma and reported a fibre-type potency difference for non-occupational exposures. A major problem with assessing fibre-type differences is the very few studies in which fibre type can be unambiguously identified. Given the paucity of studies, it is important to have data from the most recent studies with the largest number of cases. Marsh used PubMed to locate relevant studies. Of the four chrysotile studies they identified, there were one each from Egypt2 and France3 and two …

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.010
Bibliometrics0.0090.010
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.011
GPT teacher head0.255
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreCommentary

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

Citations3
Published2017
Admission routes1
Has abstractyes

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