Bridging the communication gap between oncologists and patients receiving palliative therapies.
Bibliographic record
Abstract
The discussion of prognosis is a regular component of oncology practice. When the prognosis is poor, the disclosure can be difficult for both patients and physicians alike. In a recent article by Chen et al., stage IIIB and IV lung cancer patients were surveyed on their beliefs about radiotherapy they were receiving. A significant proportion of patients expressed the belief that radiotherapy, which they were receiving purely with palliative intent, was likely to cure their cancer. Misunderstanding the goals of treatment can have important consequences with respect to informed decision-making and end-of-life planning. There are likely many factors contributing to this misunderstanding, both from the perspective of the patient as well as the physician. Discussing incurable disease in a clear, honest manner without taking away hope can be very challenging for the physician. Even when done well, patients often do not hear or completely understand the message. Focusing on active treatment may in fact perpetuate the patient's belief that they can be cured. In this article, some of the factors contributing to inaccurate beliefs are discussed. Awareness of the issue, and approaching the patient in a somewhat different manner when disclosing prognosis, may help patients to develop more appropriate beliefs about their disease and treatment. Ultimately the goal is for patients to make decisions that align with their beliefs and values, which can only be done if they have clear understanding of prognosis.
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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 | 0.024 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".