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Record W3119348353 · doi:10.2147/copd.s273406

Tea Consumption and Risk of Chronic Obstructive Pulmonary Disease in Middle-Aged and Older Singaporean Adults

2021· article· en· W3119348353 on OpenAlexaff
Tze Pin Ng, Qi Gao, Xinyi Gwee, Denise Q. L. Chua, Wan C. Tan

Bibliographic record

VenueInternational Journal of COPD · 2021
Typearticle
Languageen
FieldMedicine
TopicTea Polyphenols and Effects
Canadian institutionsUniversity of British Columbia
FundersBiomedical Research CouncilNational Medical Research CouncilMedical Research Council
KeywordsMedicinePulmonary diseaseCOPDConsumption (sociology)Internal medicineGerontologyEnvironmental health

Abstract

fetched live from OpenAlex

Objective: The anti-oxidant and anti-inflammatory actions of phytochemicals and the smooth muscle relaxant actions of theophylline present in tea may confer pulmonary protection and reduce COPD risk. We investigated tea consumption (black, oolong, or green) association with COPD risks in a population-based cohort study of older adults aged ≥55. Methods: /FVC <0.70) among 4617 participants and 920 participants free of COPD at baseline who were assessed at follow-up 4.5 years later. Results: Prevalent cases of COPD consumed less tea than their non-COPD counterparts. Estimated odds ratio (OR) and 95% confidence intervals (95% CI) of association with prevalent COPD, adjusted for age, sex, ethnicity, housing type, smoking, alcohol, physical activity and BMI declined across tea consumption levels (p-trend=0.048), and was lowest for ≥3 cups/day (OR=0.77, 95% CI=0.61-0.96). The cumulated incidence of COPD declined across tea consumption categories (p-trend=0.012) and the lowest OR of association (OR=0.35, 95% CI=0.17-0.69) with consuming ≥3 cups/day after co-variate adjustment. Conclusion: Different kinds of tea showed similar non-significant trends of associations but appeared to be strongest for green tea. Tea consumption in this Asian population was associated with lowered COPD prevalence and incidence.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.242

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)0.0000.000

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 head0.011
GPT teacher head0.263
Teacher spread0.252 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of COPDSame topicTea Polyphenols and EffectsFrench-language works237,207