Integration of end-of-life care in clinical trials for advanced malignancies.
Bibliographic record
Abstract
67 Background: ASCO endorses early integration of palliative care in the treatment of patients with advanced cancers and encourages patient education with regards to prognosis and participation in medical decision making. We implemented a uniform process of discussing end of life and advance directives (AD) early in the course of experimental cancer treatment and assessed whether this process is aligned with our patients' perspectives. Methods: This was a pilot study in a research unit conducting early-phase trials for patients with no remaining standard of care. Accrual goal was 15 patients in four months. We identified patients with advanced malignancies who were screened for cancer trials and approached their physicians with a request to fill out an AD form in discussion with their patients. Upon initiation of treatment, we filled out a supportive care checklist for each patient. The patients filled out a structured questionnaire probing their views on end-of-life discussion and awareness to supportive care resources on cycle 2 day 1 (Q1) and on cycle 3 day 1 (Q2). Results: Out of 41 screened, we completed checklists for 36 patients. Of these, 26 filled out Q1 and 11 filled out Q1 + Q2. Response rate to the questionnaires was 100%. The patients were diagnosed with metastatic solid tumor (23) and hematologic malignancies (3) and had 2 (0-5) previous lines of treatment. Females and males were 14 and 12, respectively. AD forms were filled out in most patients (81%), but about half (54%) reported having had AD discussions with their physicians. Most patients (73%) were aware of supportive resources (palliative care, social worker, support groups), and most (62%) actually used them while on study. Agreement in Q1 and Q2 was comparable (Table), with some rise in agreement with the timing of AD discussion. Conclusions: In the context of treatment on clinical trials, a large-scale initiative is feasible. More patients agree with time that early discussion of AD meets their goals while almost half report having had no such discussion with their physicians. [Table: see text]
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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 | 0.090 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".