Bacterial contamination of blood products for transfusion in the Democratic Republic of the Congo: temperature monitoring, qualitative and semi-quantitative culture.
Bibliographic record
Abstract
BACKGROUND: Bacterial contamination of blood for transfusion is rarely investigated in low-income countries. We determined the contamination rate of blood products in the Democratic Republic of the Congo. MATERIAL AND METHODS: In this prospective observational study, blood products in one rural and two urban hospitals (paediatric and general) contained a satellite sampling bag by which blood was sampled for culture in a blood culture bottle (4 mL) and on an agar-coated slide to estimate colony forming units (CFU/mL). Bacteria were identified with biochemical tests and MALDI-TOF (Bruker). Exposure time >10 °C was assessed on a subset of blood products. RESULTS: CFU/mL. Contamination rates for the urban paediatric, urban general and rural hospitals were 1.6%, 2.4% and 0.3%, respectively (p=0.004). None of the following variables was significantly associated with contamination: (i) donor type (voluntary 1.6%, family 1.2%, paid 3.9%); (ii) type of blood product (red cells 1.6%, whole blood 0.6%); (ii) season (dry season 2.4%, rainy season 1.8%); (iv) age of blood product (contaminated 8 days vs non-contaminated 6 days); and (v) exposure time >10 °C (median for contaminated and non-contaminated blood reached maximum test limit of 8 hours). DISCUSSION: A bacterial contamination rate of 1.4% of whole blood and red cells is similar to results from high-income countries. Implementation of feasible risk-mitigation measures is needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".