Survey for bacterial testing of platelet components in Latin America
Bibliographic record
Abstract
Introduction Bacterial contamination of platelet concentrates ( PC s) poses the highest post‐transfusion infectious risk in developed countries. However, there is not extensive information about similar strategies implemented in Latin America. Aim To assess the status of platelet screening for bacterial contamination in Latin American blood banks. Methods An online‐based survey with 10 comprehensive questions was sent to 46 blood banks in five countries: Argentina, Brazil, Colombia, Honduras and Mexico. The centres were asked about PC production and shelf life, and strategies to improve PC safety. Centres were also surveyed regarding postsampling quarantine, results interpretation and haemovigilance data. Results Ten centres(21·8%) in four countries answered the survey. Annual PC production ranges from 600 to 19 200. All sites store PC s for 5 days, and eight sites use leucocyte reduction. Five centres(50%) produce apheresis and platelet‐rich‐plasma PC s, one centre(10%) produces buffy coat ( BC ) PC s, one centre(10%) produces apheresis PC s, and three centres(30%) produce apheresis and BC PC s. Nine sites reported using established donor skin donor disinfection protocols, eight employ first aliquot diversion, and two visually inspect PC s. Eight sites screen PC s for bacterial contamination with culture methods: three sites(37·5%) test 1% of outdated PC s and five sites(62·5%) test 100% of PC s 20‐24 h after blood collection. Of these five sites, two quarantine PC s for 24 h after sampling. Three sites have haemovigilance data, which are unavailable to the public. No sites have implemented pathogen reduction technologies. Conclusions Screening practices and mitigation strategies vary considerably between sites highlighting the need for standardisation of procedures in Latin America.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".