Association of Past Smoking Status With Gout in Māori People in Aotearoa New Zealand
Bibliographic record
Abstract
Objective Evidence for an association of smoking with gout is conflicting. We assessed associations of current and past smoking with gout in an Aotearoa New Zealand (NZ) population. Methods Multivariable logistic regression analysis was performed on cross-sectional data from participants of NZ Māori (from 2 studies: Genetics of Gout in Aotearoa [GGA] study of 293 participants with gout and 431 without; and Ngāti Porou Hauora [NPH] study of 111 participants with gout and 42 without), Pacific people (257 participants with gout and 357 without), and European (694 participants with gout and 688 without) ancestry. Results Current smoking was not associated with gout in NZ Māori (GGA: adjusted odds ratio [aOR] 1.54,P= 0.13; NPH: aOR 3.02,P= 0.10), Pacific people (aOR 0.64,P= 0.21), or European (aOR 0.92,P= 0.80) cohorts. Ex-smoker status was associated with higher gout prevalence in Māori cohorts (GGA: aOR 1.71,P= 0.02; NPH: aOR 7.95,P< 0.001), but not in Pacific people (aOR 1.10,P= 0.69) or European (aOR 1.18,P= 0.22) cohorts. Associations were independent of age, sex, BMI, alcohol intake, kidney function, hypertension, diabetes, physical activity, sugary drink consumption, education, and employment. No association of smoker status with serum urate concentrations was observed in participants without gout. Conclusion Ex-smoker status was associated with higher gout prevalence in people of NZ Māori ancestry. No association of current smoking with gout was observed across ancestral groups, raising uncertainties about the relevance of an association specific to ex-smokers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".