Prophylactic RhCE and Kell antigen matching: impact on alloimmunization in transfusion‐dependent patients with myelodysplastic syndromes
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Thirty to 80 per cent of patients with myelodysplastic syndromes (MDS) become transfusion-dependent and are at risk for red blood cell (RBC) alloimmunization. This study compared alloimmunization rates in transfusion-dependent patients with MDS at an institution with a policy of prophylactic antigen matching for RhCE and K (PAM) with those transfused at institutions without such a policy (non-PAM). MATERIALS AND METHODS: Transfusion records were retrospectively reviewed to determine total number of RBC transfusions received, whether RBC phenotyping was performed, the type and date of first alloantibody development and receipt of prophylactic antigen matching for RhCE and K. RESULTS: In 176 transfusion-dependent patients with MDS, the overall rate of new alloimmunization was 17%; the majority of patients (87%) developed at least one alloantibody to Rh or Kell antigens. The alloimmunization rate at the institution with a PAM policy was 11% compared with 23% at non-PAM institutions (P = 0·06). The rate of Rh/K alloimmunization was 7 vs. 22%, respectively (P = 0·008). No patient who received PAM developed a Rh/K alloantibody. CONCLUSION: The rate of alloimmunization was 11% at an institution with a PAM policy which was non-significantly lower than 23% at institutions without a PAM policy. However, rates of Rh/K alloimmunization were significantly lower. Such a policy should be considered in transfusion-dependent patients with MDS, although further studies on cost-effectiveness and careful consideration of resource availability in the local context are required.
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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 | 0.000 |
| 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 | 0.000 |
| 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".