Misidentification of Cutibacterium acnes as Atopobium vaginae during routine bacterial screening of platelet concentrates using the VITEK 2 system
Bibliographic record
Abstract
Skin flora bacteria, such as Cutibacterium acnes, are the predominant contaminants of blood products used for transfusion. Platelet concentrates (PCs), a therapeutic product used to treat patients with platelet deficiencies, are stored at ambient temperature under agitation, providing ideal conditions for bacterial proliferation. PCs are screened for microbial contamination using the automated BACT/ALERT culture system. Positive cultures are processed and contaminating organisms are identified using the VITEK 2 system. Over a period of approximately 2 years, several PC isolates were identified as Atopobium vaginae to a high level of confidence. However, since A. vaginae is associated with bacterial vaginosis and is not a common PC contaminant, a retrospective investigation revealed that in all cases Cutibacterium acnes, a common PC contaminant was misidentified as A. vaginae. Our investigation revealed that the media type used to grow PC bacterial isolates can have a significant impact on the results obtained on the VITEK 2 system. Our findings support the use of macroscopic, microscopic, and other biochemical analyses to confirm the identity of unexpected species identified by the VITEK 2 system during bacterial screening of blood products.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 0.001 |
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".