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Record W2809162967 · doi:10.1016/j.envres.2018.06.003

Long-term exposure to air pollution and the incidence of multiple sclerosis: A population-based cohort study

2018· article· en· W2809162967 on OpenAlexafffund
Li Bai, Richard T. Burnett, Jeffrey C. Kwong, Perry Hystad, Aaron van Donkelaar, Jeffrey R. Brook, Karen Tu, Ray Copes, Mark S. Goldberg, Randall V. Martin, Brian J. Murray, Alexander Kopp, Hong Chen

Bibliographic record

VenueEnvironmental Research · 2018
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreEnvironment and Climate Change CanadaPublic Health OntarioUniversity Health NetworkUniversity of TorontoDalhousie UniversityToronto Western HospitalHealth CanadaMcGill UniversityInstitute for Clinical Evaluative Sciences
FundersHealth CanadaUniversity of TorontoOntario Ministry of Health and Long-Term CareInstitute for Clinical Evaluative Sciences
KeywordsMedicineIncidence (geometry)CohortPopulationCohort studyHazard ratioEnvironmental healthProportional hazards modelConfidence intervalInternal medicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.072
GPT teacher head0.356
Teacher spread0.284 · 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
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

Citations58
Published2018
Admission routes2
Has abstractno

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