Implementing and Evaluating the Impact of BoneRx: A Healthy Bone Prescription for Men with Prostate Cancer Initiating Androgen Deprivation Therapy
Bibliographic record
Abstract
Background: The initiation of Androgen Deprivation Therapy (ADT) results in rapid and profound hypogonadism, resulting in significant bone and muscle loss, increasing the risk for osteoporosis (OP), falls, and fractures. Despite this, there exist very low rates of guideline adherent care regarding bone health in this population. We developed and implemented a healthy bone prescription tool entitled BoneRx to facilitate the uptake of guideline-concordant bone health care into practice and increase patient awareness and promote the uptake of health bone behaviours (HBBs). Methods: We conducted a cross-sectional pre-BoneRx implementation (n = 143) vs. post-implementation (n = 149) cohort study to evaluate the impact on (i) bone health care, patient engagement in HBB, and patient knowledge and health beliefs regarding OP. Results: There was a significant difference pre- vs. post BoneRx implementation on receipt of baseline BMD (34.7% vs. 59.5%, p < 0.0001) and bone health counselling (32.4% vs. 59.9%, p < 0.0001). More participants in the post-BoneRx implementation cohort reported taking vitamin D supplements 57% vs. 81% (p < 0.001) and calcium supplements 39% vs. 61% (p < 0.001). Physical activity levels also significantly increased (p = 0.021). No differences were detected in OP knowledge or feelings of OP susceptibility, seriousness, or health motivation. Conclusion: BoneRx is a simple, cost-effective, and acceptable strategy that could improve the care of PCa survivors receiving ADT.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".