Evaluation of a collaborative model for successful implementation of a National CD4 enumeration EQA program in Cameroon
Bibliographic record
Abstract
Participation in an EQA program is critical to the quality assurance process. Reliable and precise CD4 T-cells enumeration are essential to improve the clinical management of patients by evaluating the disease progression and by monitoring the effectiveness of ART in HIV-patients. The CIRCB, CD4 reference laboratory, in collaboration with the Canadian QASI-program, recruited sites, distributed and analyzed CD4-panels in 61 sites across Cameroon. A trend and performance analysis in the pre-analytical, analytical and post-analytical phases was performed. Continuous training and corrective actions carried out from 2014 to 2018 increased the number of participating sites from 15 to 61 sites, the number of unacceptable results decreased from 50 to 10%. Specific challenges included errors in pre analytic (17.5%), analytic (77.0%) and post-analytic (5.5%) phases. This EQA requires the application of good laboratory practices, fluidic communication between all the stakeholders, continuous training, application of specific on-site corrective measures, and timely equipment maintenance in order to avoid repetitive errors and to increase laboratory performance. It could be extended to other HIV-1 testing like viral load and EID point-of-care. Partnership with QASI serve as a model for implementation of a successful EQA model for resource limited countries wanting to implement EQA for HIV testing and monitoring in alignment with 90-90-90 targets.
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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 | 0.059 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".