Using the serious illness conversation guide to improve the quality of life of hematology-oncology patients: a pilot study
Bibliographic record
Abstract
Introduction: Hematology-oncology patients are more likely to receive high intensity care (HIC), including ICU admission and active cancer treatment, than solid cancer patients near end of life (EOL). This prevents patients and their families from realistically planning for the future, and diminishes quality of life (QOL). We previously conducted a retrospective study to understand factors influencing HIC outcomes at EOL in hematology patients at McGill-affiliated hospitals. While non-curative goals, early level of intervention (LOI) discussions and palliative care (PC) involvement lowered the likelihood of HIC at EOL, the median time of LOI discussion and PC involvement to death was 22 and 9 days respectively. We hypothesize that a timely discussion aligning patient perspectives and goals with their treating team could improve QOL at EOL. Methods: We are conducting a pilot study looking at the impact of using the Serious Illness Conversation Guide (SICG), a validated conversation tool in the general oncology population, on the QOL of hematology patients. Participants are identified by their treating doctor or nurse practitioner to be at risk of dying in the next year. The primary aim is to decrease death in acute care. Secondary aims include reporting other HIC outcomes, time from LOI discussion and PC consult to death, and the short term benefit to QOL. In addition, qualitative analysis will explore participant perspectives on benefits of the SICG and areas to improve, and explore EOL QOL topics relevant to hematology patients. We have currently enrolled 2 patients. Interim analysis is projected for September 2023.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".