Detection of septic transfusion reactions to platelet transfusions by active and passive surveillance
Bibliographic record
Abstract
Septic transfusion reactions (STRs) resulting from transfusion of bacterially contaminated platelets are a major hazard of platelet transfusion despite recent interventions. Active and passive surveillance for bacterially contaminated platelets was performed over 7 years (2007-2013) by culture of platelet aliquots at time of transfusion and review of reported transfusion reactions. All platelet units had been cultured 24 hours after collection and released as negative. Five sets of STR criteria were evaluated, including recent AABB criteria; sensitivity and specificity of these criteria, as well as detection by active and passive surveillance, were determined. Twenty of 51,440 platelet units transfused (0.004%; 389 per million) were bacterially contaminated by active surveillance and resulted in 5 STRs occurring 9 to 24 hours posttransfusion; none of these STRs had been reported by passive surveillance. STR occurred only in neutropenic patients transfused with high bacterial loads. A total of 284 transfusion reactions (0.55%) were reported by passive surveillance. None of these patients had received contaminated platelets. However, 6 to 93 (2.1%-32.7%) of these 284 reactions met 1 or more STR criteria, and sensitivity of STR criteria varied from 5.1% to 45.5%. These results document the continued occurrence of bacterial contamination of platelets resulting in STR in neutropenic patients, failure of passive surveillance to detect STR, and lack of specificity of STR criteria. These findings highlight the limitations of reported national STR data based on passive surveillance and the need to implement further measures to address this problem such as secondary testing or use of pathogen reduction technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".