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Record W2039171607 · doi:10.1515/cclm.2008.154

Methods for the detection of bacterial contamination in blood products

2008· review· en· W2039171607 on OpenAlexfundno aff
Thomas Müller, H. Mohr, Thomas Montag

Bibliographic record

VenueClinical Chemistry and Laboratory Medicine (CCLM) · 2008
Typereview
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsnot available
FundersUniversity of Calgary
KeywordsContaminationBlood cultureMicrobiologyBiologyAntibiotics

Abstract

fetched live from OpenAlex

Culture-based and molecular assays have been developed for the screening of platelet concentrates and other blood components for bacterial contaminations. In this review, the principles of the assays are outlined. The focus of this review is the assessment of the analytical qualities of the methods. Spiking studies by adding defined levels of a wide range of bacteria to the complex biological matrix provide the first basis to evaluate and compare the qualities of methods for bacterial detection. The sensitivity acceptable for reliable screening for bacteria critically depends on the timing of either early sampling (within a period of up to 24 h after preparation of the blood component) or late sampling (a few hours before issuing the blood component). Large screening studies are essential to confirm both adequate sensitivity and specificity of the testing. In the ideal setting, these studies are prospectively planned and include systematic surveillance of adverse events in response to the administration of the screened products. The findings from sterility testing (predominantly with automated systems for detection of bacteria based on CO(2) generation) of more than 550,000 platelet concentrates in 13 studies are summarised. The limitations of the early sampling and the "negative-to-date" strategy to issue platelet concentrates are addressed. A few reported cases of probable transmission of bacteria by platelet transfusion despite negative screening tests emphasise the need to further develop optimised methods for testing of bacteria blood components.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.093
GPT teacher head0.447
Teacher spread0.353 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations36
Published2008
Admission routes1
Has abstractyes

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