BeTTer Outcomes Workgroup Health Quality Initiative to optimize bone health for prostate cancer patients in the British Columbia Cancer System
Bibliographic record
Abstract
INTRODUCTION: Bone-targeted therapies (BTTs) are integral to the management of bone metastases in metastatic castration-resistant prostate cancer (mCRPC). BTTs vary considerably in referral and drug access pathways and optimal BTT use requires multi-specialty consultation and supervision. Health quality improvement (HQI) has become the predominant framework to improve patient care in multidisciplinary settings. METHODS: HQI initiatives on use of BTT in mCRPC were developed and evaluated in five centers of a provincial cancer center network using Plan-Do-Study-Act (PDSA) methodology. Multidisciplinary teams (MDTs) completed a common quality assessment form and an HQI template and then implemented an HQI initiative. Feedback and findings were shared and discussed at regional events. It was subsequently determined whether to adopt, adapt, or abandon initiatives. RESULTS: Patterns of unmet needs varied across type of BTT. Gaps in use of radium-223 were mostly referral and education issues that could be directly addressed at the local level by participating clinician teams. Conversely, most supportive BTT gaps were related to coverage and resourcing support. HQI initiatives selected by each site consisted of implementation or expansion of local MDT meetings, referral documents, databases, and improvement charters. The main HQI initiative was completed in four sites and was adapted or adopted in three. Improvements in BTT use were observed in two of three centers with data on HQI process measures. CONCLUSIONS: Despite the overall heterogenous structure of the groups and metrics used, this study demonstrated that the PDSA framework provides the needed structure for improvements in BTT use in mCRPC across multiple sites.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".