Additional file 1 of Associations between glycated hemoglobin and the risks of incident cardiovascular diseases in patients with gout
Bibliographic record
Abstract
Additional file 1: Fig. S1. Flow diagram showing the selection of participants in this study. Table S1. Comparisons of baseline characteristics between included and excluded participants in UK biobank. Table S2. Results from sensitivity analyses for the relationship between different HbA1c groups and risk of CVD. Fig. S2. Hazard ratios for risk of CHD, stroke, and CVD death in relation to different HbAlc levels (shadows indicating 95% confidence intervals for hazard ratios). Fig. S3. Hazard ratios for risk of CVD events in relation to different HbAlc levels stratified by sex (shadows indicating 95% confidence intervals for hazard ratios). Fig. S4. Hazard ratios for risk of CVD events in relation to different HbAlc levels stratified by age group (shadows indicating 95% confidence intervals for hazard ratios). Fig. S5. Hazard ratios for risk of CVD events in relation to different HbAlc levels stratified by diabetes (shadows indicating 95% confidence intervals for hazard ratios). Fig. S6. Hazard ratios for risk of CVD events in relation to different HbAlc levels stratified by MH status (shadows indicating 95% confidence intervals for hazard ratios). Fig. S7. Sensitivity analysis results of hazard ratios for risk of CVD events, CHD, stroke, and CVD death in relation to different HbAlc levels (shadows indicating 95% confidence intervals for hazard ratios). Fig. S8. Sensitivity analysis results of hazard ratios for risk of CVD events, CHD, stroke, and CVD death in relation to different HbAlc levels (models further adjusted for urate-lowering drugs and serum urate, shadows indicating 95% confidence intervals for hazard ratios)
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.846 | 0.087 |
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".