QASI: A collaboration for implementation of an independent quality assessment programme in India
Bibliographic record
Abstract
OBJECTIVE: The HIV pandemic remains a significant global health concern. Accurate determination of CD4+ T-cells in patient samples relies on reliable CD4 enumeration. The Quality Assessment and Standardization programme for Immunological measures relevant to HIV/AIDS (QASI) programme of the Public Health Agency of Canada provides clinical laboratories from resource-limited countries with a mechanism to evaluate the quality of CD4 testing and develop the implementation of an independent national External Quality Assessment (EQA) programme. This study describes how QASI helped develop the capacity for managing a sustainable national CD4 EQA programme in India. DESIGN: Supported by the Public Health Agency of Canada and Clinton Foundation HIV/AIDS Initiative, QASI engaged with the National AIDS Control Organization and the Indian National AIDS Research Institute to assist in technology transfer in preparation for the implementation/management of an independent CD4 EQA programme. Technology transfer training was provided to support corrective actions and to improve the quality of CD4 testing. Inter-laboratory variation of EQA surveys between pre- and post-skill development was compared. RESULTS: Prior to training, coefficient of variation values were 14.7% (mid-level CD4 count controls) and 39.0% (low-level). Following training, variation was reduced to 10.3% for mid-level controls and 20.0% for low-level controls. CONCLUSION: This training assisted the National AIDS Control Organization and the Indian National AIDS Research Institute in identifying the information necessary for management of an EQA programme, and developed the foundation for India to provide corrective actions for sites with challenges in achieving reliable results for CD4 enumeration. This led to a demonstrable improvement in CD4 testing quality and illustrates how country-specific training significantly improved CD4 enumeration performance for better clinical management of HIV care in India.
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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 | 0.031 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 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".