Bacterial culture of platelets with the large volume delayed sampling approach: a narrative review
Bibliographic record
Abstract
Bacterial contamination of platelets leading to post-transfusion sepsis (PTS) represents a significant risk to patients, even in the era of bacterial culture using a sample obtained 24 hours post-collection and inoculated into a single blood culture bottle. Various approaches are available for mitigation of this risk, one of which is large volume delayed sampling (LVDS) culture. LVDS aims to increase detection of contaminated platelets compared to 24-hour, single aerobic bottle culture by increasing the sample volume in order to inoculate two or more blood culture bottles, and increasing the delay before sampling, thus allowing additional time for bacteria present in a contaminated platelet product to multiply before sampling. Three establishments have implemented LVDS as their strategy for enhancing safety of platelets. Their collective experience points to a reduction in the residual risk of PTS following transfusion of contaminated platelets when compared to historical data. LVDS as a strategy to enhance platelet safety has the advantage of simplicity when compared to various two-step approaches that involve an early culture followed by either re-culture or rapid testing. With a seven-day shelf-life, LVDS leads to decreased platelet outdates when compared to 24-hour single bottle culture with a five-day shelf-life, and an increased age of platelets at transfusion.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".