Reduction of variation in T‐cell subset enumeration among 55 laboratories using single‐platform, three or four‐color flow cytometry based on CD45 and SSC‐based gating of lymphocytes
Bibliographic record
Abstract
BACKGROUND: Enumeration of CD4(+) and CD8(+) T-cell subsets provides relevant information for diagnosis and monitoring of patients with cellular immunodeficiencies. As a result, an external quality assurance scheme was implemented in Belgium, The Netherlands, and Luxembourg in 1995. A workshop was held to train the participants in state-of-the art technology for assessment of absolute T-cell subset counts (i.e., a three or four-color, single-platform assay with lymphocyte gating based on CD45 and sideward light scatter) with the aim to achieve between-site coefficients of variation (CVs) <10% and within-site CVs <5% for > or =75% of the participants. METHODS: Three send-outs of stabilized blood from a healthy donor were distributed to 55 laboratories, each with the request to perform the standard assay on three occasions. For comparison, each laboratory performed its local technique in parallel. RESULTS: With the standard technique, between-site CVs of approximately 8% (CD3+ T cells), approximately 9% (CD4+ T cells), and approximately 10% (CD8+ T cells) were achieved. Within-site CVs were <5% for 82% (CD3+ T cells) and approximately 70% (CD4+ and CD8+ subsets) of the participants. Local techniques yielded between-site CVs of 13%-17% for CD3+, CD4+, and CD8+ T cells. CONCLUSIONS: The state-of-the-art technology for T-cell subset enumeration was implemented successfully among 55 Belgian-Dutch laboratories and resulted in significant reductions of between-site variation of absolute CD3+, CD4+, and CD8+ T-cell counts.
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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 | 0.025 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".