A Randomized Controlled Trial of a Community Pharmacist—Initiated Screening and Intervention Program for Patients at High Risk for Osteoporosis—The OsteoPharm Study
Bibliographic record
Abstract
Objective: To determine the effect of a community pharmacist-initiated screening intervention for high-risk patients on subsequent testing and treatment of osteoporosis. Methods: This was a randomized, controlled multisite trial with blinded ascertainment of outcomes in 15 community pharmacies in Alberta. Patients were recruited based on national guidelines for bone mineral density (BMD) testing, including patients 65 years or older, or between 50 to 64 years with a previous fracture or with multiple other risk factors. We excluded patients who had a BMD in the past 2 years or if they were on current treatment for osteoporosis. Patients randomized to the intervention group were given education, a quantitative heel ultrasound measurement and referred to their physician. Controls received “usual care” in the community. The primary outcome was a composite endpoint of the performance of a BMD test or a new prescription for an osteoporosis medication within 4 months. Secondary outcomes included each component of the primary outcome, calcium and vitamin D supplementation, osteoporosis-related knowledge and quality of life. Results: From November 2005 to September 2007, 561 patients were screened. Overall, 262 were randomized as follows: 129 pharmacist interventions and 133 controls. Median age was 62 years, 65% were female and 17% of patients reported a previous fracture. The primary endpoint was reached in 28 patients in the intervention group (22%) compared with 14 patients in the control group (11%) (adjusted relative increase 2.14, 95% CI 1.19-3.85; p = 0.011). Calcium intake increased significantly more among intervention patients than controls (42% vs 25%, adjusted relative increase 1.69, 95% CI 1.13-2.54, p = 0.011); vitamin D intake was also greater in the intervention group, though not significant. There was no effect on knowledge or quality of life. In multivariable models, making an osteoporosis-specific appointment with the family physician, being female and having a heel ultrasound result indicating low bone mass predicted achievement of the primary endpoint. Conclusion: A multifaceted intervention program in community pharmacies directed at patients at high risk for osteoporosis doubled the number of patients getting the primary outcome. This study highlights the role community pharmacists can play in identifying and educating patients for 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| 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) | 0.009 | 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".