Determination of optimal replicate number for validation of imprecision using fluorescence cell based assays: Proposed practical method
Bibliographic record
Abstract
Background: Assay validation includes determination of inherent imprecision across the reportable range. However specific practical guidelines for determinations of precision for cell based fluorescence assays performed on flow cytometers are currently lacking. Methods: Replicates of 10 or 20 measurements were obtained for flow cytometric assays developed for clinical IVD use, including neutrophil CD64 expression for infection/sepsis detection, fetal red cell enumeration for fetomaternal hemorrhage detection, human equilibrative nucleoside transporter 1 (hENT1) quantitation in leukocytes for possible correlation with drug responsiveness, and CD34+ hematopoietic stem cell (HSC) enumeration of apheresis products, using up to three different instrument platforms for each assay. For each assay, the mean, 95% confidence intervals of the mean (95%CI), standard deviation and coefficient of variation (CV) of sequential replicates were determined. Results: For all assays and most instrument platforms <5 replicates were found adequate to validate assay imprecision levels below the 5-10% CV for repeatability claimed by the manufacturers of these assays. Results plotted as a novel parameter derived from the 95%CI and the cumulative mean for replicates, termed variance factor (VF), provide a data driven means for determining optimal replicate numbers. Conclusions: The novel VF can provide information to guide the practical selection of optimal replicate numbers for validation of imprecision in flow cytometric assays. The optimal number of replicates was assay and instrument platform dependent. Our findings indicate 3-4 replicates are sufficient for most flow cytometric assays and instrument combinations, rather than the higher numbers suggested by CLSI guidelines for soluble analytes. © 2013 Clinical Cytometry Society.
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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 | 0.037 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".