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Record W3023525765 · doi:10.1101/2020.05.05.20092445

Smoking and the risk of COVID-19 infection in the UK Biobank Prospective Study

2020· preprint· en· W3023525765 on OpenAlexaff
Eo Rin Cho, Prabhat Jha, Arthur S. Slutsky

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiobankCoronavirus disease 2019 (COVID-19)MedicineLogistic regressionProspective cohort studyOdds ratioSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakDemographyCausality (physics)EpidemiologyOddsDiseaseRisk factorInternal medicineOutbreakVirologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Abstract Several studies suggest a lower prevalence of smoking than expected among adults with coronavirus disease (COVID-19). We conducted logistic regression analyses of the UK Biobank prospective study of 0.5 million adults followed for an average of 11 years. Compared to women, men were more likely to be tested and to test positive. In sex-stratified analyses, current smokers had higher adjusted Odds Ratios (OR) for being tested (male OR 1.60, 95%CI 1.32-1.95 and female OR 1.50,1.21-.1.86). Current smokers were more slightly more likely than never smokers to test positive for COVID-19. Further examination of smoking as a risk factor for COVID-19 is required. These must take into account reverse causality, where smokers quit to avoid disease as well as prior diseases.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.050
GPT teacher head0.343
Teacher spread0.293 · 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

Citations25
Published2020
Admission routes1
Has abstractyes

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