Ad hoc antibody modification of a validated flow cytometric immunophenotyping panel—recommendations and safeguards for clinical laboratories
Bibliographic record
Abstract
Immunophenotyping by flow cytometry is a valuable test providing important information in a timely manner. In clinical laboratories, it is performed using validated antibody panels designed to ensure consistent and accurate results. However, unforeseen situations, such as unique or unusual immunophenotypes, or supply chain issues, may necessitate ad hoc modifications to these panels. This manuscript provides guidance for performing minor modifications, such as substituting or adding one or two antibodies, while maintaining the integrity of the assay. These modifications are intended for rare clinical situations and are not substitutes for the full validation protocols outlined in CLSI H62. An example of this would be a patient with a rare, but not uncommon, situation in which a B cell lymphoma lacks expression of CD19, CD20, and surface light chains, such that the lineage of the neoplastic cells cannot be determined without a straightforward addition or substitution of another marker into a laboratory's available panel. The recommendations and best practices herein aim to optimize patient care by allowing laboratories to adapt to unique clinical scenarios without compromising assay performance and are not a way to permanently modify the assay. Key considerations include assessing the impact on fluorescence compensation, antibody binding, assay sensitivity, and overall assay performance. The manuscript provides limitations for the extent of modifications, examples, and troubleshooting strategies to ensure reliable results when ad hoc changes are made. Proper documentation with review and approval by laboratory medical directors is recommended to mitigate risks associated with these modifications.
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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 | 0.109 | 0.197 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.022 |
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".