High‐sensitivity flow cytometric assays: Considerations for design control and analytical validation for identification of Rare events
Bibliographic record
Abstract
The current consensus recommendation papers dealing with the unique requirements for the analytical validation of assays performed by flow cytometry address the validation of sensitivity (both analytical and functional) only in general terms. In this paper, a detailed approach for designing and validating the sensitivity of rare event methods is described. The impact of panel design and optimization on the lower limit of quantification (LLOQ) and suggestions for reporting data near, or below, the LLOQ are addressed. This paper serves to provide best practices for the development, optimization, and analytical validation of flow cytometric assays designed to assess rare events. Note that this paper does not discuss clinical sensitivity validation, which addresses the positive and negative predictive value of the test result.
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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 | 0.254 | 0.177 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".