Prevalence and risk factors for red blood cell alloimmunization in 175 children with sickle cell disease in a French university hospital reference centre
Bibliographic record
Abstract
Summary Patients with sickle cell disease (SCD) show a high prevalence of red blood cell (RBC) alloimmunization, but few studies have focused on children. We aimed to study the prevalence and risk factors of RBC alloimmunization in SCD children. We retrospectively analysed the medical and transfusion files for 245 SCD children hospitalized in our centre in 2014 and included 175 patients who had received at least one RBC unit in their lifetime. The main clinical and immuno‐haematological characteristics of alloimmunized and non‐alloimmunized patients were compared. The prevalence of alloimmunization was 13·7% [95% confidence interval (CI) (8·6–18·6)], and 7·4% [95% CI (3·5–11·3)] after excluding the probable irregular natural antibodies (anti‐M, anti‐Lea, anti‐Leb, anti‐Lex). Main risk factors for alloimmunization were increased number of RBC units received (median of 65 vs. 10 units per patient; P = 0·01) and the presence of one or more red cell autoantibodies (46·2% vs. 4·7%; P < 0·0001). The alloimmunization rate was higher for episodically transfused than chronically transfused patients (1·43 vs. 0·24/100 units received; P < 0·001). The presence of red cell autoantibodies appears to be a major risk factor for alloimmunization in SCD children and could justify specific transfusion guidelines.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".