Impact of the international program for quality assessment and standardization for immunological measures relevant to HIV/AIDS: QASI
Bibliographic record
Abstract
Measurements of CD4 T-cell levels are essential for the assessment of human immunodeficiency virus (HIV) disease course, clinical staging, epidemiological studies, and decisions regarding prophylactic therapies against opportunistic infection. Until now, only in the industrialized countries was T-cell subset monitoring considered a practical option to assess disease progression. The Quality Assessment and Standardization for Immunological Measures Relevant to HIV/AIDS (QASI) program was established in 1997 to meet performance assessment for immunophenotyping laboratories in countries where such service is not available. The QASI program is provided at no cost to any laboratory in a resource-poor setting that wishes to participate. This report describes the beneficial impact of participation in the QASI program. Carefully selected commercial stabilized whole blood preparations were sent regularly to participating laboratories. Participants reported the T-cell subset values they obtained by flow cytometry. Once the aggregate mean values for the T-cell subsets were established for the shipment, a comprehensive and confidential report was sent to each laboratory. The results from five consecutive shipments were analyzed. The coefficient of variation decreased from 7.2% to 4.7% and from 14.2% to 8.8% for percent and absolute CD4 T-cell counts, respectively. With the implementation of the QASI program using commercial stabilized whole blood specimens, it is possible to reduce interlaboratory error. This study illustrates that a quality assessment program can improve the overall performance of laboratories. Reducing interlaboratory variation can enhance significantly the effectiveness of multicenter HIV vaccine or drug trial evaluation.
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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 | 0.210 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".