Influence of patient perceptions and preferences for osteoporosis medication on adherence behavior in the Denosumab Adherence Preference Satisfaction study
Bibliographic record
Abstract
OBJECTIVE: This study aims to evaluate patient perceptions of subcutaneous denosumab or oral alendronate in postmenopausal women with or at risk for osteoporosis and how these perceptions influence adherence. METHODS: Postmenopausal women with low bone mass were randomized to denosumab 60 mg every 6 months for 1 year (treatment period 1 [TP1]) followed by alendronate 70 mg once weekly for 1 year (treatment period 2 [TP2]), or vice versa. Beliefs about Medicines Questionnaire data were collected at baseline and at 6, 12, 18, and 24 months; a necessity-concerns differential (NCD) was calculated for each time point. Logistic regression analyses were performed to evaluate the influences of baseline characteristics on nonadherence. RESULTS: Participants included 250 women (alendronate/denosumab, n = 124; denosumab/alendronate, n = 126). During TP1, the NCD at month 6 was higher with denosumab than with alendronate (P = 0.0076). In TP2, the NCD was higher for women switched to denosumab than for women switched to alendronate at 6 months (P = 0.0126) and 12 months (P = 0.4605). Denosumab was preferred to alendronate regardless of treatment sequence (P < 0.0001). Covariate analysis revealed that higher TP2 baseline necessity scores were associated with lower odds of nonadherence (P = 0.0055), whereas higher concerns about medication scores were associated with higher odds of nonadherence (P = 0.0247). Higher NCD scores were also associated with lower odds of nonadherence (P = 0.0015). CONCLUSIONS: Participants preferred denosumab to alendronate while on treatment and had more positive perceptions of denosumab than alendronate. These perceptions were associated with better 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".