Preliminary determination of bacterial contamination of whole blood units in a Ghanaian blood bank: Providing evidence to improve transfusion safety
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Transfusion-associated bacterial sepsis poses a significant risk to patient safety. This study aimed to determine the rate of bacterial contamination of whole blood (WB) collected at the Cape Coast Teaching Hospital (CCTH) as a quality control and quality assurance activity. MATERIALS AND METHODS: One-hundred and three WB units collected between January and April of 2018 were screened for bacterial contamination. Bacteria isolated from positive cultures were identified and subjected to antimicrobial testing. WB recipients were followed up for clinical symptoms. RESULTS: Of the 103 WB units tested, 33 (32%) were contaminated with bacteria. Gram-positive organisms accounted for 67% of the isolates, including coagulase-negative Staphylococcus, Staphylococcus aureus and Bacillus spp., while Gram-negative bacteria comprised 33% of the isolates, with Citrobacter freundii, Serratia marcescens, Escherichia coli and Providencia stuartii being identified. Resistance to antibiotics varied between species. No septic transfusion events were reported involving WB units tested in this study. CONCLUSION: The high percentage of contaminated WB units collected at the CCTH provided evidence-based data for the implementation of improved donor skin disinfection processes and the use of blood diversion pouches in 2019. These approaches allowed CCTH to comply with Ghanaian regulatory entities.
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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 | 0.004 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".