Red blood cell transfusion and associated outcomes in patients referred for palliative care: A retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: We aim to describe the occurrence of red blood cell transfusion and associated predictive factors and outcomes among patients referred for palliative care. STUDY DESIGN AND METHODS: This retrospective cohort study used linked health administrative data of adults referred for palliative care at an academic hospital from 2014 to 2018. Multivariable regression models were employed to evaluate patient characteristics associated with transfusion and the relationship between transfusion status and location of death. Survival analyses were performed using log-rank tests and Cox proportional hazards modeling. RESULTS: Of 6980 evaluated patients, 885 (12.7%) were transfused following palliative care consultation. Covariate factors associated with transfusion included younger age, higher performance status, lower baseline hemoglobin, and a diagnosis of hematologic malignancy (OR = 2.97, 95% CI 2.20-4.01) or solid organ tumor (OR = 1.37, 95% CI 1.10-1.71) vs. noncancer diagnosis. Median survival from palliative care consultation was 19 (IQR 5-75) days; 83 (32-305) days in those transfused and 15 (4-57) days in the nontransfused group (p < .0001). Median survival following transfusion was 56 (19-200) days. Solid organ tumor diagnosis was independently associated with poor survival (HR = 1.7, 95% CI 1.39-2.09 vs. non-cancer diagnosis). Among individuals who survived ≥30 days, transfusion was associated with a higher likelihood of death in hospital (OR = 2.15, 95% CI 1.71-2.70 vs. home/subacute setting). DISCUSSION: Transfusions commonly occurred in patients receiving palliative care, associated with cancer diagnoses and favorable baseline prognostic factors. Poor survival following transfusion, particularly in solid organ tumor patients, and the twofold likelihood of death in hospital associated with this intervention have important implications in prescribing transfusion for this population.
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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 | 0.004 |
| 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) | 0.001 | 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".