Questions prompt lists used by palliative care teams help trigger discussions on prognosis and end-of-life issues with advanced cancer patients.
Bibliographic record
Abstract
12110 Background: Accuracy of prognosis perception is a key element to allow advanced cancer patients to make informed decisions and to reflect on their end-of-life priorities. This study aims to explore whether a question prompt list can promote discussions on prognosis and end-of-life issues during palliative care consultations for advanced cancer patients. Methods: In this multicentric randomised study, patients assigned in the interventional arm receive a question prompt list during the first palliative care consultation (T1) after referral by oncologists. The primary endpoint is the number of questions asked by patients during the second palliative care consultation (T2) one month later. Secondary objectives are anxiety and depression, quality-of-life, satisfaction with care, coping assessed at baseline (T1) and at two months (T3). Palliative care teams from 3 french comprehensive cancer centers participate in the study. Main inclusion criteria were adult patients with metastatic non-haematological cancer referred to the palliative care team and with an estimated life expectancy less than one year. Results: Patients (n = 71) in the QPL arm asked more questions (mean 21.8 versus 18.2, p-value = 0.03) during the palliative care consultations compared to patients in the control arm (n = 71). These questions addressed palliative care (mean 5.6 versus 3.7, p-value = 0.012) and end-of-life issues (mean 2.2 versus 1, p = 0.018) more frequently than in the control arm. At two months, compared to baseline, there was no change in anxio-depressive symptoms or quality of life. Conclusions: QPL favours discussion on prognosis and end-of-life care during the palliative care consultations for advanced cancer patients. Clinical trial information: NCT02854293 .
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".