Red cell transfusion in outpatients with myelodysplastic syndromes: a feasibility and exploratory randomised trial
Bibliographic record
Abstract
Optimal red cell transfusion support in myelodysplastic syndromes (MDS) has not been tested and established. The aim of this study was to demonstrate feasibility of recruitment and follow-up in an outpatient setting with an exploratory assessment of quality of life (QoL) outcomes (EORTC QLQ-C30 and EQ-5D-5L). We randomised MDS patients to standardised transfusion algorithms comparing current restrictive transfusion thresholds (80 g/l, to maintain haemoglobin 85-100 g/l) with liberal thresholds (105 g/l, maintaining 110-125 g/l). The primary outcomes were measures of compliance to transfusion thresholds. Altogether 38 patients were randomised (n = 20 restrictive; n = 18 liberal) from 12 participating sites in UK, Australia and New Zealand. The compliance proportion for the intention-to-treat population was 86% (95% confidence interval 75-94%) and 99% (95-100%) for restrictive and liberal arms respectively. Mean pre-transfusion haemoglobin concentrations for restrictive and liberal arms were 80 g/l (SD6) and 97 g/l (SD7). The total number of red cell units transfused on study was 82 in the restrictive and 192 in the liberal arm. In an exploratory analysis, the five main QoL domains were improved for participants in the liberal compared to restrictive arm. Our findings support the feasibility and need for a definitive trial to evaluate the effect of different red cell transfusion thresholds on patient-centred outcomes.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".