Rate of Bone Density Change Does Not Enhance Fracture Prediction in Routine Clinical Practice
Bibliographic record
Abstract
CONTEXT: There is contradictory information on whether the rate of bone mineral density (BMD) loss is an independent risk factor for osteoporotic fractures and whether this should be included in fracture prediction systems. OBJECTIVE: This study was undertaken to better define rate of BMD loss as a contributor to fracture risk in routine clinical practice. DESIGN AND SETTING: We performed a retrospective cohort study using a database of all clinical BMD results for the province of Manitoba, Canada. PATIENTS: We included 4498 untreated women age 40 yr and older at the time of a second BMD test performed between April 1996 and March 2009. MAIN OUTCOME MEASURES: A total of 146 women with major osteoporotic fracture outcomes after the second BMD test (mean observation, 2.7 yr) and relevant covariates were identified in population-based computerized health databases. RESULTS: Annualized percentage change in total hip BMD was no greater in fracture compared to nonfracture women (-0.4 ± 1.7 vs. -0.5 ± 1.4; P = 0.166). After adjustment for final total hip BMD, other covariates, and medication use, rate of total hip BMD change did not predict major osteoporotic fractures (hazard ratio, 0.95 per sd decrease; 95% confidence interval, 0.81-1.10). Similar results were also seen in analyses based upon change in lumbar spine and femoral neck BMD. CONCLUSIONS: We found no evidence that BMD loss, as detected during routine clinical monitoring, was a significant independent risk factor for major osteoporotic fractures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".