Early palliative care versus usual haematological care in multiple myeloma: retrospective cohort study
Bibliographic record
Abstract
OBJECTIVES: Although early palliative care (EPC) is beneficial in acute myeloid leukaemia, little is known about EPC value in multiple myeloma (MM). We compared quality indicators for palliative and end-of-life (EOL) care in patients with MM receiving EPC with those of patients who received usual haematological care (UHC). METHODS: This observational, retrospective study was based on 290 consecutive patients with MM. The following indicators were abstracted: providing psychological support, assessing/managing pain, discussing goals of care, promoting advance care plan, accessing home care services; no anti-MM treatment within 14 and 30 days and hospice length of stay >7 days before death; no cardiopulmonary resuscitation, no intubation, <2 hospitalisations and emergency department visits within 30 days before death. Comparisons were performed using unadjusted and confounder-adjusted regression models. RESULTS: 55 patients received EPC and 231 UHC. Compared with UHC patients, EPC patients had a significantly higher number of quality indicators of care (mean 2.62±1.25 vs 1.12±0.95; p<0.0001)); a significant reduction of pain intensity over time (p<0.01) and a trend towards reduced aggressiveness at EOL, with the same survival (5.3 vs 5.46 years; p=0.74)). CONCLUSIONS: Our data support the value of integrating EPC into MM routine practice and lay the groundwork for future prospective comparative studies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".