Prognostic Disclosure in Metastatic Breast Cancer: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Prognostic disclosure enables shared decision-making by keeping patients informed about their health and involved in treatment-related choices. In metastatic cancer, patients who understand their prognosis are better placed to evaluate their health situation, have more realistic expectations about treatment, and better end-of-life (EoL) care. However, many oncologists feel uncomfortable discussing prognoses with their patients, as they worry about causing psychological distress and diminishing hope. Other barriers to prognostic disclosure are associated with the inherent uncertainty of prognostication, which is especially prominent in metastatic breast cancer (mBC), as are a lack of timely discussions about EoL care and poor quality of care at EoL. Despite this background, a preliminary literature search has shown that, to date, knowledge about prognostic disclosure in mBC has not been systematically mapped. OBJECTIVE: The overall aim of this scoping review will be to comprehensively explore the existing literature pertaining to prognostic disclosure in mBC. METHODS: This scoping review will follow Arksey and O'Malley's expanded framework. We will systematically search electronic databases for peer-reviewed, published journal articles to identify appropriate studies. First, 2 members of the research team will independently review titles and abstracts. Then, they will review full texts to establish whether articles meet inclusion criteria. A data chart for collecting and sorting information will be developed. Results will be reported following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and summarized quantitatively and qualitatively. RESULTS: We expect to present the results in a scoping review in 2025. CONCLUSIONS: To our knowledge, this scoping review will be the first systematic effort aimed at mapping existing literature on prognostic disclosure in mBC, in which rapidly developing therapeutic approaches and changing disease trajectories generate new uncertainties and place new communicative demands on oncologists. The insights gained from this scoping review will inform the development of an interview schedule with oncologists, oncology residents, oncology nurses, and patients with mBC to explore their experiences, views, and perceptions about prognostic disclosure. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/57256.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".