Vertebral Fracture Assessment Increases Use of Pharmacologic Therapy for Fracture Prevention in Clinical Practice
Bibliographic record
Abstract
The impact of vertebral fracture assessment (VFA) on lateral spine images in clinical practice on subsequent patient use of fracture prevention medication is unknown. Our objective was to determine the association of prevalent vertebral fracture identified on bone density lateral spine images (positive VFA) with subsequent use of fracture prevention therapy in usual clinical practice, using the Manitoba Bone Density Program database prospective observational cohort. Since 2010, targeted VFA imaging has been done at the time of bone densitometry in Manitoba for 21% of women and men meeting criteria based on age, bone mineral density (BMD), height loss, and glucocorticoid use. Among 6652 treatment-naive individuals with at least 90 days follow-up who had VFA imaging, 923 (13.9%) had one or more definite vertebral fractures identified using a modified algorithm-based qualitative (ABQ) method. For those with a positive VFA, their bone density reports stated the patient was at high risk of subsequent fracture and qualified for fracture prevention therapy. Subsequent osteoporosis treatment initiated within the next 12 months was identified using population-based pharmacy data. Logistic regression models were used to estimate the association of positive VFA with subsequent prescription (Rx), compared to negative VFA. Fracture prevention medication was started by 2127 (32%) individuals, 52.3% with positive versus 28.4% with negative VFA (p value <0.001). This association was substantially stronger in those designated (before VFA results were known) to have low or moderate fracture risk compared to high fracture risk (interaction p value <0.001), and in those with osteopenia (OR 4.51; 95% CI, 3.48 to 5.85) compared to those with osteoporosis by BMD criteria (OR 1.72; 95% CI, 1.43 to 2.08, interaction p value <0.001). Targeted VFA imaging at the time of bone densitometry substantially improves identification of those at high fracture risk and fracture prevention medication use among those with prevalent vertebral fracture. © 2019 American Society for Bone and Mineral Research. © 2019 American Society for Bone and Mineral Research.
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 | 0.001 | 0.018 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".