Effect of Total Hip Bone Area on Osteoporosis Diagnosis and Fractures
Bibliographic record
Abstract
DXA is affected by skeletal size, with smaller bones giving lower areal BMD despite equal material density. Whether this size effect confounds the use of BMD as a diagnostic and fracture risk assessment tool is unclear. We identified 16,205 women of white ethnicity >or=50 yr of age undergoing baseline hip assessment with DXA (1998-2002) from a population-based database that contains all clinical DXA test results for the Province of Manitoba, Canada. Total hip measurements were categorized according to quartile in total hip bone area (Q1 = smallest, Q4 = largest). Longitudinal health service records were assessed for the presence of nontraumatic osteoporotic fracture codes during a mean of 3.2 yr of follow-up after BMD testing (757 osteoporotic fractures, 186 hip fractures). Total hip bone area strongly affected osteoporosis diagnosis with much higher rates in Q1 (14.4%) than Q4 (8.9%). However, incident fracture rates were constant across all area quartiles, and prevalent fractures were paradoxically fewer in smaller area quartiles (p < 0.001 for trend). Age was a potential confounder that correlated positively with area (r = 0.12, p < 0.0001). When age was not included in a Cox regression model, Q1 seemed to have a lower rate of incident osteoporotic fractures (HR = 0.80, 95% CI = 0.66-0.98, reference Q4) and hip fractures (HR = 0.63, 95% CI = 0.43-0.94) for a given level of BMD. In age-adjusted regression models, total hip BMD was strongly predictive of incident osteoporotic fractures (HR per SD = 1.83, 95% CI = 1.68-1.99) and hip fractures (HR per SD = 2.80, 95% CI = 2.33-3.35), but there was no independent effect of bone area (categorical or continuous). Nested matched subgroup analysis and ROC analysis confirmed that bone area had no appreciable effect on incident fractures. We conclude that total hip areal BMD categorizes a substantially higher fraction of women with smaller bone area as being osteoporotic despite younger age. Incident fracture rates correlate equally well with BMD across all bone area quartiles when adjusted for age.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".