Sunset haematology: Improving the end-of-life journey for patients and caregivers, in patients with haematologic malignancies
Bibliographic record
Abstract
BACKGROUND AND AIM Haematologic Malignancies (HM) are diverse diseases with differing illness trajectories and therapeutic pathways. Unfortunately HM patients may rapidly and unexpectedly clinically deteriorate, resulting in suboptimal engagement of palliative and end-of-life (EOL) care. Compared to patients with solid tumours, HM patients have many different factors affecting their end-of-life (EOL) journey. Uniquely, a subset of HM patients with bone marrow failure (BMF) can be supported for significant, but highly variable, periods of time with red blood cell transfusions (RBCT), platelet transfusions (PT) and prophylactic antibiotics. Availability of chronic RBCTs and PTs make HM patients with BMF similar to elderly and poor prognosis patients with end stage kidney disease (ESKD). Multidisciplinary Palliative Supportive Care programs have been shown to be effective for these EKSD patients and may serve as supportive care models for EOL journey in HM patients. This project is a pilot study aiming to provide a template for management of EOL for patients with HM with BMF, and their care-givers. METHODS Three components are being developed: 1) Survey of patient opinions around treatment decision-making. 2) Analyses of the impact of patient, disease and treatment factors on the probability of survival from start of PT, to inform patients. 3) Collaborative involvement between Haematology and Palliative Care staff involved in the local ESKD program, to develop a template for earlier EOL pathway planning in HM patients. CONCLUSION Progress of work to date will be presented including preliminary findings and next steps.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".