The BEST criteria improve sensitivity for detecting positive cultures in residual blood components cultured in suspected septic transfusion reactions
Bibliographic record
Abstract
BACKGROUND: Culturing residual blood components after suspected septic transfusion reactions guides management of patients and cocomponents. Current practice, accuracy of provider vital sign assessment, and performance of the AABB culture criteria are unknown. A multicenter international study was undertaken to investigate these issues and develop improved culture criteria. STUDY DESIGN AND METHODS: Retrospective data for all transfusion reactions resulting in residual blood component culture in 2016 were collected from participating hospitals. The performance of the AABB culture criteria were assessed for detection of positive culture results. Modifications to the AABB criteria including 1) recommending culturing in the setting of isolated high fevers, 2) defining hypotension and tachycardia using objective parameters, and 3) incorporating antipyretic use were tested to determine if modifications improved performance. Modifications associated with improvement were incorporate into the BEST criteria. The AABB and the BEST criteria were then tested against a data set enriched for positive culture results to determine which criteria were superior. RESULTS: Data were collected from 20 centers encompassing 779,143 transfusions, 3,187 reported transfusion reactions, and 1,104 cultured components. There was marked variation in reaction reporting and culturing rates (0.0%-100.0%). Of 35 total positive component cultures, only one of 35 (2.9%) had concordant patient cultures; 12 of 34 (35.3%) did not have patient cultures performed. The BEST criteria had better sensitivity for detection of a positive culture result compared to the AABB criteria (74% vs. 41%), although specificity decreased (45% vs. 65%). CONCLUSION: Compared to the AABB criteria, the BEST criteria have improved sensitivity for positive culture detection.
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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 | 0.014 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".