Patient and Physician Decisional Factors Regarding Hypercalcemia of Malignancy Treatment: A Novel Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: Integrating shared decision making between patients and physicians and incorporating their values and preferences in the development of clinical practice guidelines (CPGs) is of critical importance to optimize CPG implementation and treatment adherence. This applies to many debilitating diseases, including hypercalcemia of malignancy (HCM). OBJECTIVE: Evaluate patient and physician values, preferences, and attitudes to better inform CPGs to treat HCM in adults. METHODS: We followed a mixed-methods approach. We conducted a systematic review using 5 databases to identify studies reporting on patient and physician values, costs and resources, feasibility, acceptability, and equity regarding HCM treatment. We also gathered data from different countries on the cost of multiple treatment modalities. We collected data on outcome prioritization from the CPG Working Group. Similarly, we collected data from patients with HCM regarding outcome prioritization and administered a questionnaire to evaluate their attitudes and perceptions toward treatment as well as treatment acceptability and feasibility. RESULTS: In the systematic review, we included 2 cross-sectional surveys conducted on the same population of physicians who agreed that treating HCM alleviates symptoms and improves quality of life; however, harms and benefits should be thoroughly considered when deciding on the duration of treatment. We also included 2 studies on cost showing that intravenous (IV) bisphosphonate is more cost-effective than a combination of IV bisphosphonate and calcitonin and administration of IV zoledronic acid at home is more cost-effective than other IV bisphosphonates. The cost of zoledronic acid, denosumab, and cinacalcet varied widely among countries and types (brand vs generic). Both the CPG Working Group and patients with HCM agreed that the most important outcomes when deciding on treatment were survival and resolution of HCM, but there was some variability in the ratings for other outcomes. CONCLUSION: Using mixed methods, CPG developers can obtain meaningful information regarding evidence to decision criteria. In the case of HCM CPGs, this approach has provided the required contextual information and supported the development of evidence-based recommendations.
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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 | 0.046 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.013 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".