Why Does Rate of Bone Density Loss Not Predict Fracture Risk?
Bibliographic record
Abstract
CONTEXT: Intuitively, rapid bone mineral density (BMD) loss should predict fracture risk independently of current BMD, but studies have not confirmed this. We hypothesized that measurement error when characterizing rates of BMD loss might explain this paradox. OBJECTIVE: To examine the importance of measurement error in predicting BMD loss. DESIGN AND SETTING: Retrospective registry study using BMD results for Manitoba, Canada. PATIENTS: Untreated women age 50 years and older with three femoral neck BMD tests. MAIN OUTCOME MEASURES: Correlation in annualized rates of BMD change for interval 1 (first to second scan) versus interval 2 (second to third scan) with confirmatory model-based simulations that varied measurement error and testing intervals. RESULTS: Five hundred forty two women with a mean age of 62 years had BMD measurements separated by a mean of 3.5 years for interval 1 and 3.4 years for interval 2. Mean femoral neck BMD loss was stable (-0.5% per year for interval 1, -0.6% per year for interval 2) with a weak negative correlation between intervals (r = -0.11, P = .01). There were no significant correlations for BMD change at the total hip (r = 0.01, P = .74) or total spine (r = -0.01, P = .77). Simulations showed low explained variation for BMD change between intervals 1 and 2 (<20%). To explain 50% of the variation of BMD change between intervals 1 and 2 required a BMD measurement error ≤ 0.008 g/cm(2) or a BMD testing interval ≥ 5 years. CONCLUSIONS: The low correlation between past and future BMD loss helps explain why the rate of BMD loss is unlikely to be helpful for refining 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 | 0.042 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".