The Disconnect Between Better Quality of Glucocorticoid-induced Osteoporosis Preventive Care and Better Outcomes: A Population-based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: The quality of glucocorticoid-induced osteoporosis (GIOP) care [defined by bone mineral density (BMD) testing or osteoporosis treatment] is suboptimal and has been targeted for improvement. The assumption that improvements in GIOP preventive care will lead to better outcomes has not been tested. METHODS: We used linked healthcare databases to conduct a population-based study of all adults 20 years of age or older in Manitoba, Canada, who initiated longterm (> 90 days) systemic glucocorticoids (GC) between 1998 and 2008. High-quality GIOP care was defined by BMD testing or prescription osteoporosis treatment within 6 months. Outcomes were adjusted odds of major fractures within 1 year and 3 years. RESULTS: We studied 15,285 subjects who had just begun to take GC; 5804 (38%) were 70 years of age or older, 9185 (58%) were women, and 4755 (30%) received 10 mg or more prednisone equivalents daily. Overall, 3898 (25%) subjects received a BMD test or osteoporosis treatment within 6 months. Within 1 year of starting GC, there had been 206 major fractures (1%) and within 3 years, 553 major fractures (4%). High-quality GIOP preventive care was not associated with a reduced risk of major fractures within 1 year (adjusted OR 1.6, 95% CI 1.2-2.1) or within 3 years (adjusted OR 1.3, 95% CI 1.1-1.6). CONCLUSION: Three-quarters of those initiating GC received suboptimal osteoporosis care. Conventional administrative database analyses could not demonstrate that better GIOP preventive care was associated with reductions in medically attended fractures. Clinically rich databases and different analytic techniques are needed to better evaluate the effectiveness of GIOP preventive care.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".