Compatibility of Stabilized Whole Blood Products with CD4 Technologies and Their Suitability for Quality Assessment Programs
Bibliographic record
Abstract
BACKGROUND: CD4 T cell enumeration is the most widely used prognostic marker for management of HIV disease. Internal quality control and external quality assessment (EQA) programs are critical to ensure reliability of clinical measurements. The utility of stabilized whole blood products (SWBP) as a test reagent for EQA programs such as Quality Assessment and Standardization for Immunological measures relevant to HIV/AIDS (QASI) program have been demonstrated previously. Since then, several new commercial SWBPs and alternative CD4 enumeration technologies have become available. Seven SWBPs were evaluated on seven different enumeration platforms to determine which product(s) are most suitable for EQA programs that support multiple analytical technologies. METHOD: Assessment of SWBPs was based on two criteria: (1) accuracy of CD4 T cell measurements and; (2) stability under sub optimal storage conditions. RESULTS: Three SWBPs (Multi-Check, StatusFlow and CD4 Count) showed accurate CD4 T-cell absolute count and percentage values across six of the enumeration platforms. All products retain stability up to 18 days at 21-23°C with the exception of Multi-Check-high on FacsCount and Multi-Check-Low and StatusFlow-Low on Pima. One of the products (CD4 Count) retained stability for three days on all platforms tested when stored at 37°C. CONCLUSION: This study demonstrated that the characteristics of commercially available SWBPs vary across multiple CD4 platforms. The compatibility of testing panels for EQA programs with multiple analytical platforms needs to be carefully considered, especially in large multiplatform CD4 EQA programs. The selection of a suitable cross-platform SWBP is an increasing challenge as more reagents and platforms are introduced for CD4 T-cell enumeration.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".