QASI, an international quality management system for CD4 T‐cell enumeration focused to make a global difference
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: A significant worldwide mobilization effort to treat people with HIV disease began in 2003. Most guidelines for initiating antiretroviral therapy require reliable and reproducible CD4 T-cell counting. Therefore, any effort that improves global availability of quality managed assessment schemes for CD4 T-cell enumeration is a positive achievement for the clinical management of AIDS on a worldwide scale. METHODS: The Canadian QASI-Quality Management System (QMS) has been in operation for over a decade. More recently, QMS has fine-tuned its strategy to optimize its global impact in the fight against the HIV/AIDS pandemic. Three modifications were implemented: (1) introduction of skills and knowledge transfer workshops pertaining to the initiation of national quality management programs for CD4 counting, (2) introduction of a road map to establish domestic EQAP for countries that are ready, and (3) introduction of a statistical analysis package which permits continuous monitoring of global impact of the QASI-QMS. RESULTS: Based on QASI-QMS distribution of specimens over four consecutive participation cycles, there was decreased interlaboratory variation for both low and medium CD4 T-cell levels. After three cycles of consecutive participation, there is an average of 38 and 26% error reduction reported for the mid and low CD4 levels, respectively. CONCLUSION: The program improvements mentioned earlier appear to have had a profound effect with regard to enhancing the performance of laboratories participating in the QASI-QMS. Specifically, there is a significant reduction in interlaboratory variability of CD4 T-cell counts resulting from continuous participation in the QASI-QMS.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it