Vertebral Fractures: Which Radiological Criteria Are Better Associated With the Clinical Course of Osteoporosis?
Bibliographic record
Abstract
Study Purpose: Morphometric methods categorize potential osteoporotic vertebral fractures (OVF) on the basis of loss of vertebral height. A particular example is the widely used semiquantitative morphometric tool proposed by Genant (GSQ). A newer morphologic algorithm-based qualitative (mABQ) tool focuses on vertebral end-plate damage in recognizing OVF. We used data from both sexes in the Canadian Multicentre Osteoporosis Study (CaMos) to compare the 2 methods in identifying OVF at baseline and during 10 years of follow-up. Materials and Methods: We obtained lateral thoracic and lumbar spinal radiographs (T4-L4) 3 times, at 5-year intervals, in 828 participants of the population-based CaMos. Logistic regressions were used to study the association of 10-year changes in bone mineral density (BMD) with incident fractures. Results: At baseline, 161 participants had grade 1 and 32 had grade 2 GSQ OVF; over the next 10 years, only 9 of these participants had sustained incident GSQ OVF. Contrastingly, 21 participants at baseline had grade 1 and 48 grade 2 mABQ events; over the next 10 years, 79 subjects experienced incident grade 1 or grade 2 mABQ events. Thus, incident grades 1 and 2 morphologic fractures were 8 times more common than morphometric deformities alone. Each 10-year decrease of 0.01 g/cm 2 in total hip BMD was associated with a 4.1% (95% CI: 0.7-7.3) higher odds of having an incident vertebral fracture. Conclusions: This analysis further suggests that morphometric deformities and morphologic fractures constitute distinct entities; morphologic fractures conform more closely to the expected epidemiology of OVF.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".