Measuring prognostic awareness in patients with advanced cancer: a scoping review and interpretive synthesis of the impact of hope
Bibliographic record
Abstract
BACKGROUND: Assessment of prognostic awareness (PA) in patients with advanced cancer is challenging because patient responses often indicate their hopes. The objectives of this scoping review were to summarize studies that measured PA in patients with advanced cancer and to synthesize data about how PA was measured and whether hope was incorporated into the measurement. METHODS: MEDLINE and Embase databases were searched from inception to December 14, 2021. Data regarding the impact of hope on assessment of PA were extracted when studies reported on patients' beliefs about prognosis and patients' beliefs about their doctor's opinion about prognosis. An interpretive synthesis approach was used to analyze the data and to generate a theory regarding the incorporation of hope into the assessment of PA. RESULTS: In total, 52 studies representing 23 766 patients were included. Most were conducted in high-income countries and measured PA based on the goal of treatment (curable vs incurable). Five studies incorporated hope into the assessment of PA and reported that among patients who responded that their treatment goal was a cure, an average of 30% also acknowledged that their doctors were treating them with palliative intent. Interpretive synthesis of the evidence generated a trinary conceptualization of PA patients who are aware and accepting of their prognosis; aware and not accepting; and truly unaware. Each of these groups will benefit from different types of interventions to support their evolving PA. CONCLUSION: The trinary conceptualization of PA may promote understanding of the impact of hope in the assessment of PA and guide future research.
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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 | 0.064 | 0.228 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.024 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".