Automated red blood cell exchange with a post‐procedure haematocrit targeted at 34% in the chronic management of sickle cell disease
Bibliographic record
Abstract
Optimal targets for red blood cell exchange (RCE) are not well defined in the chronic management of sickle cell disease. We analysed transfusion requirements and iron-related outcomes in 101 patients on chronic RCE with a post-procedure haematocrit (Ht) targeted at 34%, which is higher than typically used. A majority were of HbSS/HbSβ0 genotype (n = 72) and enrolled for neurological complications (n = 53). Fifty patients had a positive Ht balance with RCE (>2% mean increase from pre-procedure level), while 43 patients maintained a neutral balance. The first group required fewer red blood cell units/year (65 vs. 80, p < 0.001), but a significant proportion were iron overloaded based on R2* with liver MRI (32% vs. none performed) and prescription of iron chelation (52% vs. 0%, p < 0.001, after a median of 19 months). The second group was more likely to receive iron supplementation (6% vs. 56%, p < 0.001). Chronic automated RCE with a post-procedure Ht targeted at 34% is not iron-neutral, and personalized Ht goals may be more appropriate in certain settings. This higher target should be compared with a lower Ht strategy in individuals with similar baseline red cell volumes to assess iron homeostasis and blood product requirements.
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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 | 0.002 |
| 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 | 0.001 | 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 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".