A flow cytometric method for platelet counting in platelet concentrates
Bibliographic record
Abstract
BACKGROUND: The platelets (PLTs) in PLT concentrates are counted with hematology analyzers, but varying results among different hematology analyzers are observed, making comparisons very difficult. Due to the absence of red blood cells in PLT concentrates, the International Council for Standardization in Hematology (ICSH) reference method was modified to be used for PLT concentrates and validated in an international comparative study. STUDY DESIGN AND METHODS: Five PLT samples were shipped to eight participating centers of the Biomedical Excellence for Safer Transfusion (BEST) Collaborative and counted on the same day. PLTs were stained with fluorescein isothiocyanate-labeled anti-CD41a in tubes (TruCount, BD Biosciences), measured on a flow cytometer, and analyzed with a uniform template. These samples were also counted on 15 hematology analyzers. RESULTS: The ICSH method and newly developed BEST method yielded PLT counting results with less than 1% difference (not significant). The intercenter coefficient of variation (CV) of the BEST method was on average 6.3% versus 7.6% on average for hematology analyzers. The CV of individual hematology analyzers was on average 0.9%, which was considerably lower than for the flow cytometers with a mean of 3.7%. CONCLUSION: The BEST flow cytometric method has a smaller intercenter CV and a smaller center-to-center deviation from the group mean compared to hematology analyzers. Conversely, individual hematology analyzers are more precise than the flow cytometric method. Thus, the flow cytometric method provides a calibration tool to allow comparisons between centers, but there is no need to replace routine counting with hematology analyzers.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".