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Record W2394863005 · doi:10.1097/jom.0000000000000604

Maternal Exposure to Air Pollution and Adverse Birth Outcomes in Halifax, Nova Scotia

2015· article· en· W2394863005 on OpenAlexafffundabout
Abbey E. Poirier, Linda Dodds, Trevor Dummer, Daniel Rainham, Bryan Maguire, Markey Johnson

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of British ColumbiaNova Scotia Health AuthorityDalhousie UniversityHealth Canada
FundersIWK Health CentreNova Scotia Health Research Foundation
KeywordsNova scotiaNova (rocket)Environmental scienceEnvironmental healthMedicineDemographyGeographyAeronauticsEngineeringArchaeology

Abstract

fetched live from OpenAlex

OBJECTIVE: We aimed to examine the associations between exposure to air pollution exposure and the outcomes of preterm birth (PTB), term low birth weight (TLBW), and small for gestational age. METHODS: We conducted a population-based cohort study using a perinatal database linked to land-use regression-modeled air pollution data. RESULTS: Compared with women in the lowest quartile of toluene exposure, those in the second lowest quartile showed a positive association with PTB (odds ratio = 1.35, 95% confidence interval: 1.12, 1.63). A piecewise logistic regression breakpoint analysis identified a cut point (identifying a change in the slope) of 0.36 μg/m for toluene and the risk of PTB. There was also some evidence to suggest an association between sulfur dioxide and TLBW. CONCLUSIONS: This study provides some evidence to suggest that in an area of relatively low air pollution concentration, maternal exposure to some air pollutants may be associated with adverse birth outcomes.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.051
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.050
GPT teacher head0.321
Teacher spread0.271 · 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

Citations38
Published2015
Admission routes3
Has abstractyes

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