Implementation and Operational Research
Bibliographic record
Abstract
BACKGROUND: A plethora of point-of-care (POC) tests exist in the HIV and tuberculosis diagnostic pipeline which require rigorous evaluation to ensure performance in the field. The accuracy and feasibility of nurse-operated multidisciplinary-POC testing for HIV antiretroviral therapy (ART) initiation/monitoring was evaluated. METHODS: Random HIV-positive adult patients presenting at 2 treatment clinics in South Africa for ART initiation/monitoring were consented and enrolled. POCT was performed by a dedicated nurse on a venipuncture specimen; Pima (CD4), HemoCue (hemoglobin), Reflotron (alanine aminotransferase, creatinine), Accutrend (lactate) and compared with laboratory testing. External quality assessment, training, workflow, and errors were assessed. RESULTS: n = 324 enrolled at site1; n = 469 enrolled at site 2. Clinical data on n = 305 participants: 65% (n = 198) female with a mean age of 39.8 (21-61) years; mean age of males 43.2 (26-61) years; 70% of patients required 3 or more POC tests/visit. External quality assessment material was suitable for POCT. CD4, hemoglobin and alanine aminotransferase testing showed good agreement with predicate methodology; creatinine and lactate had increased variability. Pima CD4 misclassified up to 11.6% of patients at 500 cells per microliter and reported 4.3%-6% error rate. A dedicated nurse could perform POCT on 7 patients/day; inclusion of Pima CD4 increased time for testing from 6 to 110 minutes. Transcription error rate was 1%. CONCLUSIONS: Nurses can accurately perform multidisciplinary POCT for HIV ART initiation/monitoring. This will however, require a dedicated nurse as current duties will increase if POC is added to workflow. The use of Pima CD4 will increase patients initiated on ART. Connectivity will be central to ensure quality management of results, but overall impact will need to still be addressed.
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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 | 0.061 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.009 |
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".