Apparent “Rapid Loss” After Short-Interval Bone Density Testing in Menopausal Women Is Usually a Measurement Artifact
Bibliographic record
Abstract
CONTEXT: Medication may be considered when bone mineral density (BMD) loss is reported as "excessive." OBJECTIVE: We hypothesized that the rate of BMD change between 2 serial tests demonstrates higher random variability at shorter vs longer intervals, misclassifying some women as "rapid losers." METHODS: This retrospective observational cohort study in Manitoba, Canada included women aged > 55 years without osteoporosis medications or glucocorticoids. Using paired baseline (1998-2016) and repeat (2001-2018) BMD measurements, we estimated the distribution of annualized change (first to second BMD) at spine, hip, and femoral neck stratified by testing interval (2-2.9, 3-3.9,...9-9.9, ≥ 10.0 years). "Rapid annual bone loss" was defined as exceeding the 95th percentile for decreases from all measurement pairs. Odds ratios (OR) for rapid loss were estimated using regression models adjusted for age and clinical covariates. RESULTS: From 7126 paired BMD measurements, mean annualized change was constant yet standard deviations in BMD change were > 2-fold greater with intervals of 2 to 2.9 years vs ≥ 10 years(P < 0.001). "Rapid annual loss" was seen in ~10% of short-interval tests vs < 1% of long-interval tests. ORs for "rapid loss" progressively declined with increasing testing interval (spine 15.3 [4.8-48.9], total hip 9.3 [4.4-19.5], femoral neck 18.7 [6.8-51.3] for a 2- to 2.9-year testing interval; referent ≥ 10 years). CONCLUSION: There is a wider apparent range in annualized BMD loss with short-interval testing which greatly attenuates over longer intervals. BMD reports of "rapid loss" across shorter testing intervals likely reflect an artifact of BMD measurement error and should not be used as an indication for antifracture medication initiation.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".