Evaluation of a reporting system for bacterial contamination of blood components in the United States
Bibliographic record
Abstract
BACKGROUND: The transfusion of blood components contaminated with bacteria may have serious clinical consequences, but few data are available on the incidence of these events. A national effort to assess the frequency of blood component bacterial contamination associated with transfusion reaction (the BaCon Study) was initiated to better estimate their occurrence. STUDY DESIGN AND METHODS: Standard reporting criteria, data collection forms, and a standardized reporting protocol were developed in collaboration with the American Red Cross, AABB, and the Department of Defense. Episodes reported to the BaCon Study were compared with those reported to the FDA's national reporting systems to estimate the extent to which all serious reactions associated with bacterial contamination were captured. RESULTS: During the first 2 years, 38 episodes meeting study criteria were reported; 21 were laboratory-confirmed. The estimated proportion of episodes reported to the BaCon Study (i.e., completeness of coverage) was lower than that reported to the FDA during the same period (0.33 vs. 0.68), but the positive predictive value was higher (0.66 vs.0.28). CONCLUSION: Despite the complexity of obtaining reports from a large number of United States hospitals and transfusion centers, the feasibility and usefulness of the BaCon Study were shown. This study was the only national study in the United States to monitor adverse clinical events associated with bacterial contamination of blood components. By building on hospital-based reporting of transfusion-related adverse events, the BaCon Study serves as a model for the study of other complications associated with blood and blood components.
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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 | 0.279 | 0.288 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".