Application of the ADVIA cerebrospinal fluid assay to count residual red blood cells in blood components
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: There is no automated, accurate assay for the enumeration of residual red blood cells (rRBCs) in non-RBC components for transfusion, despite the potential risk of allo-immunization when mismatched components are transfused. MATERIALS AND METHODS: The automated ADVIA 120 cerebrospinal fluid (CSF) assay, which is approved to count RBCs and WBCs in CSF samples, was optimized and tested to measure rRBC in platelet concentrate (PC) and plasma components. RESULTS: Sample dilution, incubation time and reagent volume were optimized for use with non-RBC blood products. The assay was linear (R(2) = 0·99), even at low rRBCs counts. Intra- and inter-assay variation gave coefficients of variance (CV) between 2·2 and 9·4% and 2·6 and 14·9%, respectively, depending on rRBC levels. Good correlation (r = 0·995) was found between the automated assay and manual counting, which is considered the gold standard. Using the automated assay, the range of rRBCs (count/unit) in buffy-coat platelet concentrate (PCs) was 27-5505 × 10(6) and in apheresis PCs was 1-361 × 10(6). CONCLUSION: The ADVIA CSF assay is a sensitive, precise and accurate means to assess rRBC counts in non-RBC components.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".