Targeted Coaching to Improve Osteoporosis Therapy Adherence: A Single Arm Variation of the C-STOP Study
Bibliographic record
Abstract
BACKGROUND: In this pre-planned variation of the Comparing Strategies Targeting Osteoporosis to Prevent Fractures After an Upper Extremity Fracture (C-STOP) trial, we investigated whether adherence-specific coaching by the case manager (CM) further improved the adherence and persistence rates compared to those seen in the C-STOP trial. METHODS: We conducted a prospective observational cohort study of community-dwelling adults 50 years or older who suffered an upper-extremity fracture and were not previously treated with osteoporosis medications, to assess whether a well-trained CM can partner with patients to improve adherence to and persistence with oral bisphosphonate intake. The primary outcome was adherence (taking > 80% of prescribed doses) to oral bisphosphonate intake at 12 months after study enrollment. Secondary outcomes included primary adherence to and 12-month persistence with oral bisphosphonate and calcium and vitamin D supplement intake at 12 months. RESULTS: The study cohort consisted of 84 participants, of which 30 were prescribed an oral bisphosphonate. Twenty-two (73.3%) started treatment within 3 months. The adherence rate at 12 months was 77.3%. The persistence rate at 12 months was 95.5%. Of those not prescribed an oral bisphosphonate, 62.8% were taking supplemental calcium and 93.0% were taking supplemental vitamin D at 12 months. Depression was a significant predictor of 12-month non-adherence (adjusted odds ratio, 9.8; 95% confidence interval, 1.2-81.5). CONCLUSIONS: Adherence-specific coaching by a CM did not further improve the level of medication adherence achieved in the original C-STOP study. Importantly, these results can inform adherence in future intervention studies.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".