The Togo national proficiency test pilot programme for basic clinical chemistry tests
Bibliographic record
Abstract
Background: A national proficiency test (PT) programme is not currently implemented in most low-income countries. However, participation in such PT programmes assists improves test performance and result accuracy.Objective: This study assessed how well 11 government hospital laboratories performed 18 basic clinical chemistry tests and identified areas needing improvement.Methods: A cross-sectional study was carried out by the Division of Laboratories of the Ministry of Health of Togo from 01 July 2016 to 31 December 2016. The test performance was evaluated using panels provided by One World Accuracy, Canada (Vancouver). The Clinical Laboratory Improvement Amendments criteria were used in evaluating the laboratories, and their success rates were compared with the World Health Organization Regional Office for Africa’s target of 80%.Results: The overall rate of acceptable results at the laboratories was over 80% for glucose, alanine aminotransferase, aspartate aminotransferase, gamma-glutamyltransferase, alkaline phosphatase and triglycerides tests. The laboratories using fully automated spectrophotometers had an acceptable results rate of 89% (p = 0.001). The overall performance of the laboratories by cycles varied from 71% to 82%.Conclusion: This national PT programme identified the tests, which laboratories must improve their performance (urea, creatinine, uric acid, bilirubin, cholesterol, total protein, calcium, magnesium, phosphorus). It demonstrated the need for the use of routine appropriate internal quality control in all laboratories. The proficiency test programme should be extended to all clinical laboratories and target all biology disciplines.
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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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".