Additional file 2 of Development of genome-wide polygenic risk scores for lipid traits and clinical applications for dyslipidemia, subclinical atherosclerosis, and diabetes cardiovascular complications among East Asians
Bibliographic record
Abstract
Additional file 2: Table S1. Clinical characteristics of all participants. Table S2. Correlations of candidate polygenic risk scores with total cholesterol in validation datasets. Table S3. Correlations of candidate polygenic risk scores with triglyceride levels in validation datasets. Table S4. Correlations of candidate polygenic risk scores with HDL cholesterol in validation datasets. Table S5. Correlations of candidate polygenic risk scores with LDL cholesterol in validation datasets. Table S6. Correlations between measured lipid traits and polygenic risk scores derived by using the genome-wide significant variants identified in European populations. Table S7. Prediction ability of the best polygenic risk scores for abnormal lipid levels in testing datasets. Table S8. Prediction ability of the best polygenic risk scores for abnormal lipid levels in validation datasets. Table S9. Baseline and follow-up clinical characteristics of the adolescents included in the assessment of three-year changes for lipid traits. Table S10. Clinical characteristics of the adult women included in the assessment of intima-media thickness. Table S11. Clinical characteristics of the T2D patients included in the assessment of coronary heart disease. Table S12. Association between coronary heart disease and quintiles of polygenic risk scores in T2D patients. Table S13. Correlations of candidate polygenic risk scores with total cholesterol in T2D patients. Table S14. Correlations of candidate polygenic risk scores with triglyceride levels in T2D patients. Table S15. Correlations of candidate polygenic risk scores with HDL cholesterol in T2D patients. Table S16. Correlations of candidate polygenic risk scores with LDL cholesterol in T2D patients. Table S17. Baseline clinical characteristics of the adolescents stratified by the status of follow-up.
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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 | 0.004 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.780 | 0.098 |
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".