History matters: Preventing severe allergic transfusion reactions
Bibliographic record
Abstract
OBJECTIVE: Prior studies have shown that pretransfusion medication is not effective in preventing allergic transfusion reactions (ATRs), but these studies did not consider the patient's history of ATR. This study evaluated whether pretransfusion antiallergy medications decrease the chance of ATRs in patients with a history of severe ATR. METHODS: This single-center, retrospective study investigated the effect of pretransfusion medications on preventing ATRs in patients with a history of at least 1 severe ATR between March 2018 and January 2024. Patient demographics as well as clinical and transfusion reaction data were collected from our electronic health record (EHR) system. Data were analyzed using SPSS (IBM Corp) and machine learning in Python, version 3.12.4. RESULTS: In our cohort, 53 patients aged 5 weeks to 94 years with 2767 analyzable transfusion encounters had experienced 88 lifelong mild and severe ATRs. Premedication (P = .021), regular antiallergy medication (P < .001), and washing/volume reduction (P = .032) were associated with a statistically significantly lower chance of developing ATRs in our patient population. CONCLUSIONS: Patients with at least 1 severe ATR benefit from pretransfusion administration of antiallergy medications.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".