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Record W3161697888 · doi:10.1136/thoraxjnl-2020-216223

Chronic airflow obstruction and ambient particulate air pollution

2021· article· en· W3161697888 on OpenAlexaff
André F.S. Amaral, Peter Burney, Jaymini Patel, Cosetta Minelli, Filip Mejza, David M. Mannino, Terence Seemungal, P A Mahesh, Li Cher Lo, Christer Janson, Sanjay Juvekar, Meriam Denguezli, Imed Harrabi, Emiel F.�M. Wouters, Hamid Hacene Cherkaski, Kevin Mortimer, Rain Jögi, Eric D. Bateman, Elaine Fuertes, Mohammed Al Ghobain, Wan C. Tan, Daniel Obaseki, Asma El Sony, Michael Studnicka, Althea Aquart-Stewart, Parvaiz A Koul, Hervé Lawin, Asaad Ahmed Nafees, Olayemi Awopeju, Gregory E. Erhabor, Þórarinn Gíslason, Tobias Welte, Amund Gulsvik, Rune Nielsen, Louisa Gnatiuc, Ali Kocabaş, Guy B. Marks, Talant Sooronbaev, Bertrand Hugo Mbatchou Ngahane, Cristina Bárbara, A. Sonia Buist

Bibliographic record

VenueThorax · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of British Columbia
FundersCancer Research UKWellcome TrustUniversity of KentuckyWellcomeGlaxoSmithKlineAstraZenecaPfizer
KeywordsMedicineParticulatesEnvironmental healthAir pollutionPovertyPulmonary diseaseInternal medicineEconomic growth

Abstract

fetched live from OpenAlex

Smoking is the most well-established cause of chronic airflow obstruction (CAO) but particulate air pollution and poverty have also been implicated. We regressed sex-specific prevalence of CAO from 41 Burden of Obstructive Lung Disease study sites against smoking prevalence from the same study, the gross national income per capita and the local annual mean level of ambient particulate matter (PM 2.5 ) using negative binomial regression. The prevalence of CAO was not independently associated with PM 2.5 but was strongly associated with smoking and was also associated with poverty. Strengthening tobacco control and improved understanding of the link between CAO and poverty should be prioritised.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.293
Teacher spread0.266 · 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

Citations16
Published2021
Admission routes1
Has abstractyes

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