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Record W3087179810 · doi:10.2450/2020.0108-20

Bacterial contamination of blood products for transfusion in the Democratic Republic of the Congo: temperature monitoring, qualitative and semi-quantitative culture.

2020· article· en· W3087179810 on OpenAlexaff

Bibliographic record

VenuePubMed · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsHôpital Saint-Luc
FundersVlaamse regering
KeywordsContaminationDemocracyBlood cultureBlood transfusionIntensive care medicineMedicinePolitical scienceImmunologyBiologyMicrobiologyLawEcologyAntibiotics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.270
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2020
Admission routes1
Has abstractyes

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