Diagnostic Accuracy of the HemoCue Hb 301, STAT-Site MHgb and URIT-12 Point-of-Care Hemoglobin Meters in a Central Laboratory and a Community Based Clinic in Durban, South Africa
Bibliographic record
Abstract
In South Africa, various point-of-care hemoglobin meters are used. However, the regulatory framework for approval, implementation and oversight of use of point-of-care hemoglobin meters is suboptimal. We assessed the diagnostic accuracy of the HemoCue Hb 301, STAT-Site MHgb and URIT-12 point-of-care hemoglobin meters, compared to a central laboratory based reference assay, in a central laboratory and a community based clinic in Durban, South Africa. Differences in performance of the point-of-care assays, compared to the reference assay, were more pronounced in the community based clinic. Results were reasonable for the HemoCue Hb 301, but poor for the STAT-Site MHgb and the URIT-12. Poor test performance of point-of-care hemoglobin meters, and inadequate evaluations and oversight in South Africa, leads to suboptimal clinical care and clinical research, and increased costs. There is a need for proper evaluation and quality assurance of point-of-care tests, the results of which should be made widely available to key stakeholders.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".