How to break bad news?: Systematic Review
Bibliographic record
Abstract
Purpose: We conducted a systematic review of studies that focus on existing protocols for oncologists and other physicians who are in touch with cancer patients.Method:We searched all internationally published articles on the introduction of a protocol as the guideline. To this purpose, we did a thorough search of Pubmed and Cochrane Collaboration Library databases and reviewed all articles from 2010 to 2017.Results:We introduced 7 papers from 7 countries and evaluated their proposed protocols. A primary protocol called SPIKES had been discussed in most studies. This protocol emphasized the six steps of setting, perception, invitation, knowledge, empathy and summary.Conclusion: BBN is a balanced action that requires oncologists and other specialists to consistently adapt to its different criteria. Developing the ability to personalize and adapt to therapeutic treatment with respect to communications can be a major step forward in the training and exercises that physicians receive in connection to communication skills.
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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 | 0.037 | 0.140 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.008 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".