Flow cytometric method transfer: Recommendations for best practice
Bibliographic record
Abstract
As with many aspects of the validation and monitoring of flow cytometric methods, the method transfer processes and acceptance criteria described for other technologies are not fully applicable. This is due to the complexity of the highly configurable instrumentation, the complexity of cellular measurands, the lack of qualified reference materials for most assays, and limited specimen stability. There are multiple reasons for initiating a method transfer, multiple regulatory settings, and multiple context of use. All of these factors influence the specific requirements for the method transfer. This recommendation paper describes the considerations and best practices for the transfer of flow cytometric methods and provides individual case studies as examples. In addition, the manuscript emphasizes the importance of appropriately conducting a method transfer on data reliability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.139 | 0.241 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.013 | 0.006 |
| Research integrity | 0.018 | 0.014 |
| Insufficient payload (model declined to judge) | 0.029 | 0.051 |
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".