Additional file 1 of Associations between vaping and self-reported respiratory symptoms in young people in Canada, England and the US
Bibliographic record
Abstract
Additional file 1: Table S1. Sample description and respiratory symptoms by characteristic for the full sample and for those who had not used other inhaled products in the past 30 days (unweighted data). Table S2. Respiratory symptoms in the past week broken down by other product use, weighted n (%). Table S3. Vaping product characteristics and respiratory symptoms by characteristic among those who had vaped in the past 30 days (unweighted data). Table S4. Associations between past-30-day smoking and/or vaping, lifetime/current vaping, number of days vaped in the past 30 days and any respiratory symptoms (weighted data). Table S5. Associations between past-30-day smoking and vaping, lifetime/current vaping, number of days vaped in the past 30 days and individual respiratory symptoms (weighted data). Table S6. Associations between vaping characteristics and any respiratory symptoms (weighted data). Table S7. Associations between vaping characteristics and individual respiratory symptoms (weighted data). Table S8. Sensitivity analysis. Associations between country and any respiratory symptoms for the full sample and those who had vaped in the past 30 days (weighted data). Table S9. Interaction models for country (weighted data). Table S10. Supplementary analysis. Associations between country and individual respiratory symptoms for the full sample and those who had vaped in the past 30 days (weighted data).
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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 | 0.002 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.731 | 0.057 |
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".