Effects of femoral neck width and hip axis length on incident hip fracture risk: a registry-based cohort study
Bibliographic record
Abstract
BMD measured with DXA is widely used in clinical practice to assess fracture risk and guide management. DXA can also assess hip geometry, including femoral neck width (FNW) and hip axis length (HAL), which have both been associated with increased risk for hip fracture independently from BMD. Our objective was to assess if FNW predicts hip fracture independently from other factors including HAL. We performed a retrospective cohort study using the Province of Manitoba BMD registry. The study population comprised 75 095 individuals (90.8% women), mean age 64.7 yr, with baseline hip BMD and hip geometry parameters. Linked health records were used to ascertain subsequent hospitalization with hip fracture as a primary diagnosis. During a mean follow-up of 8.3 (SD 5.1) yr, 2341 incident hip fractures were recorded. Each SD increase in age- and sex-adjusted FNW was associated with incident hip fracture (HR 1.15, 95% CI 1.10-1.19), which was unchanged after adjustment for height, weight, FN BMD, and clinical risk factors. However, FNW showed a significant positive correlation with HAL (r = 0.68). When further adjusted for HAL, FNW was no longer associated with increased risk for hip fracture (HR 0.98, 95% CI 0.94-1.03). A similar pattern was seen for FN, and intertrochanteric and non-hip fractures. In contrast, increased risk of hip fracture was consistently seen with each SD increase in HAL even after adjustment for all covariates including FNW (HR 1.35, 95% CI 1.28-1.42). In conclusion, FNW is a risk factor for hip fracture before but not after adjustment for HAL. HAL, on the other hand, robustly and independently predicts hip fracture, including both FN and trochanteric fractures.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".