The association between iliocostal distance and the number of vertebral and non-vertebral fractures in women and men registered in the Canadian Database For Osteoporosis and Osteopenia (CANDOO)
Bibliographic record
Abstract
BACKGROUND: The identification of new methods of evaluating patients with osteoporotic fracture should focus on their usefulness in clinical situations such that they are easily measured and applicable to all patients. Thus, the purpose of this study was to examine the association between iliocostal distance and vertebral and non-vertebral fractures in patients seen in a clinical setting. METHODS: Patient data were obtained from the Canadian Database of Osteoporosis and Osteopenia (CANDOO). A total of 549 patients including 508 women and 41 men participated in this cross-sectional study. There were 142 women and 18 men with prevalent vertebral fractures, and 185 women and 21 men with prevalent non-vertebral fractures. RESULTS: In women multivariable regression analysis showed that iliocostal distance was negatively associated with the number of vertebral fractures (-0.18, CI: -0.27, -0.09; adjusted for bone mineral density at the Ward's triangle, epilepsy, cerebrovascular disease, inflammatory bowel disease, etidronate use, and calcium supplement use) and for the number of non-vertebral fractures (-0.09, CI: -0.15, -0.03; adjusted for bone mineral density at the trochanter, cerebrovascular disease, inflammatory bowel disease, and etidronate use). However, in men, multivariable regression analysis did not demonstrate a significant association between iliocostal distance and the number of vertebral and non-vertebral fractures. CONCLUSIONS: The examination of iliocostal distance may be a useful clinical tool for assessment of the possibility of vertebral fractures. The identification of high-risk patients is important to effectively use the growing number of available osteoporosis therapies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".