Patients’ values and other decisional factors regarding treatment of hypercalcemia of malignancy: A Systematic Review Protocol
Bibliographic record
Abstract
Abstract Background Hypercalcemia of malignancy is the second most common cause of hypercalcemia and is associated with significant morbidity and mortality. Several treatment options are available including pharmacological therapy with bisphosphonates, denosumab, glucocorticoids, and calcimimetics, as well as conventional therapy with hydration and possibly calcitonin. While guidelines have previously considered treatment effects, no guideline has yet considered a range of contextual factors impacting recommendations for the management. Objective The aim of this study is to summarize the available evidence on important decisional factors for the development of guidelines for the treatment of hypercalcemia of malignancy. These include patient’s values and preferences, cost, acceptability, feasibility, and equity. Methods/Design: This is a systematic review of observational studies, case series, trials, reviews and qualitative studies involving treatment of adult patients with hypercalcemia of malignancy. We developed and executed two independent search strategies using five databases: Pubmed, Medline (OVID), Embase.com, CINAHL (EBSCO) and Cochrane, and reviewed their combined output. Two reviewers screened titles and abstracts and full texts and will implement data abstraction from relevant studies independently and in duplicate. The outcomes of interest are the decisional factors that influence drug selection, with possible subgroup analyses by drug class or etiology of hypercalcemia of malignancy. We will present the data collected in a narrative and thematic approach. Discussion This systematic review will identify important treatment decisional factors, and will assist guidelines panels, physicians and patients to decide on therapeutic options based on the current evidence.
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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 | 0.087 | 0.098 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.014 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.049 | 0.005 |
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".