Additional file 1 of Improved prediction of fracture risk leveraging a genome-wide polygenic risk score
Bibliographic record
Abstract
Additional file 1: Fig. S1: Distribution of discrepancies in minor allele frequencies of SNPs between the China Kadoorie Biobank and the UK Biobank; Fig. S2: Distribution of standardized gSOS among 90,172 individuals of European ancestry; Fig. S3: Cumulative incidence of major osteoporotic fracture in (A) the MrOS US cohort and (B) the SOF cohort, and cumulative incidence of hip fracture in (C) the MrOS US cohort and (D) the SOF cohort; Table S1. Summary of model training and selection in the UK Biobank; Table S2. Summary of Pearson correlation between gSOS and clinical risk factors; Table S3. Summary of incidence of osteoporotic fracture; Table S4. Summary of predictive power of gSOS and clinical risk factors; Table S5. Details of net reclassification improvement of major osteoporotic fracture risk prediction using FRAX-gSOS; Table S6. Details of net reclassification improvement of hip fracture risk prediction using FRAX-gSOS.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.809 | 0.156 |
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".