Automation for clinical CD4 T‐cell enumeration, a desirable tool in the hands of skilled operators
Bibliographic record
Abstract
BACKGROUND: Automation in HIV clinical flow cytometry when appropriately applied brings considerable standardisation benefits. The Canadian Immunology Quality Assessment Program (CIQAP) detected situations where operators did not manually override automated software in the event of improper output on the Epics XL and FC500 CD4 immunophenotyping platforms. The automated gating algorithm identifies lymphocytes using a double gate strategy based on CD45 × side scatter (SS) gating and a light scatter FS × SS gate known to fail with sub optimal specimens. METHOD: To generate correct interpretation and results CIQAP introduced a simple protocol modification, bypassing the light scatter gate to include all cells characterized by the CD45 gate. Seventeen problem cases were reanalysed for both absolute and relative T-cell subsets accuracy and compared to the CIQAP group mean values. Results were found to be associated with the percentage of lymphocytes excluded by the automated light scatter gate. RESULTS: The modified manual protocol resolved poor performance in 14 instances out of 17 problem cases. It was found to improve accuracy when the light scatter gate excluded greater than 5% of the cells. The remaining three cases had a lymphocyte recovery of greater than 94.6% in the original automated analysis. CONCLUSION: There is a risk in relying solely on automated gating procedures when using the Epics XL and FC500 CD4 immunophenotyping platforms. Laboratory managers have the responsibility to intervene when required. EQA providers are equally responsible to alert the clinical laboratories of the need to update operator training to deal with stressed specimens. © 2016 International 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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".