The impact of pharmacist interventions on osteoporosis management: a systematic review
Bibliographic record
Abstract
UNLABELLED: We completed a systematic review of the literature to examine the impact of pharmacist interventions in improving osteoporosis management. Results from randomized controlled trials suggest that pharmacist interventions may improve bone mineral density testing and calcium intake among patients at high risk for osteoporosis. INTRODUCTION: Pharmacists play a key role in many healthcare systems by helping patients manage chronic diseases. We completed a systematic review of the literature to identify randomized controlled trials (RCTs) that have examined the impact of pharmacy interventions in narrowing two gaps in osteoporosis management: identifying at-risk individuals and improving adherence to therapy. METHODS: We searched the electronic databases of EMBASE, HealthStar, International Pharmaceutical Abstracts, MEDLINE, and PubMed from database development to April 2010, examined grey literature, and completed manual searches of reference lists to identify English-language research that examined osteoporosis management interventions within pharmacy practice. Results from RCTs were abstracted and assessed for bias. RESULTS: We identified 25 studies that examined pharmacist interventions in osteoporosis management: 16 cohort, 5 cross-sectional, 1 historical/ecological control, and 3 RCTs. RCT interventions included osteoporosis educational and counseling programs, screening by pharmacists based on risk factor assessment or bone mineral density testing, and physician contact or recommendations for patients to follow-up with a general practitioner. Results from the three RCTs suggest that pharmacist interventions may improve bone mineral density testing (targeted screening) and calcium intake among patients at high risk for osteoporosis. However, two of the three RCTs had high risk of bias, and no study examined the impact of pharmacist intervention on osteoporosis treatment adherence. CONCLUSIONS: Data support the potential role for pharmacists to help reduce gaps in osteoporosis management through improved identification of high-risk patients. More research is needed to examine pharmacist interventions on osteoporosis treatment adherence.
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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 | 0.021 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".