Community based intervention to optimize osteoporosis management: randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Osteoporosis-related fractures are a significant public health concern. Interventions that increase detection and treatment of osteoporosis are underutilized. This pragmatic randomised study was done to evaluate the impact of a multifaceted community-based care program aimed at optimizing evidence-based management in patients at risk for osteoporosis and fractures. METHODS: This was a 12-month randomized trial performed in Ontario, Canada. Eligible patients were community-dwelling, aged ≥55 years, and identified to be at risk for osteoporosis-related fractures. Two hundred and one patients were allocated to the intervention group or to usual care. Components of the intervention were directed towards primary care physicians and patients and included facilitated bone mineral density testing, patient education and patient-specific recommendations for osteoporosis treatment. The primary outcome was the implementation of appropriate osteoporosis management. RESULTS: 101 patients were allocated to intervention and 100 to control. Mean age of participants was 71.9 ± 7.2 years and 94% were women. Pharmacological treatment (alendronate, risedronate, or raloxifene) for osteoporosis was increased by 29% compared to usual care (56% [29/52] vs. 27% [16/60]; relative risk [RR] 2.09, 95% confidence interval [CI] 1.29 to 3.40). More individuals in the intervention group were taking calcium (54% [54/101] vs. 20% [20/100]; RR 2.67, 95% CI 1.74 to 4.12) and vitamin D (33% [33/101] vs. 20% [20/100]; RR 1.63, 95% CI 1.01 to 2.65). CONCLUSIONS: A multi-faceted community-based intervention improved management of osteoporosis in high risk patients compared with usual care. TRIAL REGISTRATION: This trial has been registered with clinicaltrials.gov (ID: NCT00465387).
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".