Simultaneously Modelling Clustered Marginal Counts and Multinomial Proportions with Zero Inflation with Application to Analysis of Osteoporotic Fractures Data
Bibliographic record
Abstract
Summary Osteoporotic fractures are known to be highly recurring. We investigate bone-dependent and bone-independent risk factors of osteoporotic fracture frequency and relative proportions at various body locations by using the data from the osteoporotic fracture study that was conducted by the National Health and Nutrition Examination Survey, 2007–2008. We propose a new zero-inflated baseline category multinomial mixed model to characterize the clustered count responses and multinomial proportions by subject simultaneously while taking account of zero inflation and randomness of cluster sizes. Our approach gives additional insights into the risk factors of osteoporotic fracture frequencies at various body locations. This joint modelling of fracture frequency also allows us to characterize relative proportion patterns at various body locations by subject between men and women across age. These findings have clear policy relevance to appropriate osteoporotic fracture prevention and resource allocation.
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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 | 0.052 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".