Automation for clinical CD4 T‐cell enumeration, a desirable tool in the hands of skilled operators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it