A North American multilaboratory study of CD4 counts using flow cytometric panleukogating (PLG): A NIAID-DAIDS Immunology Quality Assessment Program Study
Bibliographic record
Abstract
BACKGROUND: The global HIV/AIDS pandemic and guidelines for initiating anti-retroviral therapy (ART) and opportunistic infection prophylaxis demand affordable, reliable, and accurate CD4 testing. A simple innovative approach applicable to existing technology that has been successfully applied in resource-challenged settings, PanLeukogated CD4 (PLG), could offer solutions for cost saving and improved precision. METHODS: Day-old whole blood from 99 HIV+ donors was simultaneously studied in five North-American laboratories to compare the performance of their predicate methods with the dual-platform PLG method. The predicate technology included varying 4-color CD45/CD3/CD4/CD8 protocols on different flow cytometers. Each laboratory also assayed eight replicate specimens of day-old blood from 10 to 14 local donors. Bias and precision of predicate and PLG methods was studied between- and within-participating laboratories. RESULTS: Significantly (P < 0.0001) improved between-laboratory precision/coefficient of variation (CV%) was noted using the PLG method (overall median 9.3% vs. predicate median CV 13.1%). Within-laboratory precision was also significantly (P < 0.0001) better overall using PLG (median 4.6% vs. predicate median CV 6.2%) and in 3 of the 5 laboratories. PLG counts tended to be 11% smaller than predicate methods (P < 0.0001) for shipped (median of predicate-PLG = 31) and local specimens (median of predicate-PLG = 23), both overall and in 4 of 5 laboratories (median decreases of 4, 16, 20, and 21% in shipped specimens); the other laboratory had a median increase of 5%. CONCLUSION: Laboratories using predicate CD4 methods similar to those in this study could improve their between-laboratory and their within-laboratory precision, and reduce costs, by switching to the PLG method after adequate training, if a change (usually, a decrease) in CD4 counts is acceptable to their health systems.
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".