Prediction of Vertebral Fractures Is Specific for Gender and Site of Bone Mineral Density Measurement
Bibliographic record
Abstract
OBJECTIVE: To investigate basic assumptions of prediction models for future vertebral fractures. METHODS: Lateral radiographs of the spine were obtained from 314 Portuguese individuals aged 60 years or older (205 women and 109 men) with bone mineral density (BMD) measurements at several sites. Associations between BMD at various sites, participant characteristics, and vertebral fractures were investigated. For men and women separately, logistic regression analyses and analyses of areas under the receiver-operating characteristic (ROC) curves were performed to determine the accuracy of BMD measurement at predicting the presence of vertebral deformities. RESULTS: BMD measurements at all sites significantly predicted the presence of osteoporotic vertebral deformities in women but not in men. Similarly, in analyses of areas under ROC curves, BMD assessments were statistically significantly related to vertebral deformities in women but not in men. In multivariate analyses, BMD measurements of the lumbar spine and of the forearm, adjusted for gender, age, and body mass index, significantly predicted the presence of vertebral deformity, but BMD of the hip sites did not. CONCLUSION: Prediction of fractures is specific for gender and site of BMD measurement. This challenges the use of similar algorithms for men and women as well as the use of hip BMD data to accurately estimate future vertebral fracture risk.
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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 | 0.016 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".