Amustaline‐glutathione pathogen‐reduced red blood cell concentrates for transfusion‐dependent thalassaemia
Bibliographic record
Abstract
Transfusion-dependent thalassaemia (TDT) requires red blood cell concentrates (RBCC) to prevent complications of anaemia, but carries risk of infection. Pathogen reduction of RBCC offers potential to reduce infectious risk. We evaluated the efficacy and safety of pathogen-reduced (PR) Amustaline-Glutathione (A-GSH) RBCC for TDT. Patients were randomized to a blinded 2-period crossover treatment sequence for six transfusions over 8-10 months with Control and A-GSH-RBCC. The efficacy outcome utilized non-inferiority analysis with 90% power to detect a 15% difference in transfused haemoglobin (Hb), and the safety outcome was the incidence of antibodies to A-GSH-PR-RBCC. By intent to treat (80 patients), 12·5 ± 1·9 RBCC were transfused in each period. Storage durations of A-GSH and C-RBCC were similar (8·9 days). Mean A-GSH-RBCC transfused Hb (g/kg/day) was not inferior to Control (0·113 ± 0·04 vs. 0·111 ± 0·04, P = 0·373, paired t-test). The upper bound of the one-sided 95% confidence interval for the treatment difference from the mixed effects model was 0·005 g/kg/day, within a non-inferiority margin of 0·017 g/kg/day. A-GSH-RBCC mean pre-transfusion Hb levels declined by 6·0 g/l. No antibodies to A-GSH-RBCC were detected, and there were no differences in adverse events. A-GSH-RBCCs offer potential to reduce infectious risk in TDT with a tolerable safety profile.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".