End of life in haematology: quality of life predictors – retrospective cohort study
Bibliographic record
Abstract
OBJECTIVES: Haematology patients are more likely to receive high intensity care near end of life (EOL) than patients with solid malignancy. Previous authors have suggested indicators of quality EOL for haematology patients, based on a solid oncology model. We conducted a retrospective chart review with the objectives of (1) determining our performance on these quality EOL indicators, (2) describing the timing of level of intervention (LOI) discussion and palliative care (PC) consultation prior to death and (3) evaluating whether goals of therapy (GOT), PC consultation and earlier LOI discussion are predictors of quality EOL. METHODS: We identified patients who died from haematological malignancies between April 2014 and March 2016 (n=319) at four participating McGill University hospitals and performed retrospective chart reviews. RESULTS: We found that 17% of patients were administered chemotherapy less than 14 days prior to death, 20% of patients were admitted to intensive care, 14% were intubated and 5% were resuscitated less than 30 days prior to death, 18% of patients received blood transfusion less than 7 days prior to death and 67% of patients died in an acute care setting. LOI discussion and PC consultation occurred a median of 22 days (IQR 7-103) and 9 days (IQR 3-19) before death. Patients with non-curative GOT, PC consultation or discussed LOI were significantly less likely to have high intensity EOL outcomes. CONCLUSIONS: In this study, we demonstrate that LOI discussions, PC consults and physician established GOT are associated with quality EOL outcomes for patients with haematological malignancies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".