Review of Shanghai glycohemoglobin harmonization program
Bibliographic record
Abstract
Objective To review the results of inter-laboratory comparisons in Shanghai glycohemoglobin harmonization program from 2010 to 2018, and to analyze the evolution of quality levels of HbA1c determination, so as to provide the reference for improving the HbA1c determination quality in China. Methods Retrospective analysis. The comparison data of Shanghai Glycohemoglobin Harmonization Program from 2010 to 2018 was collected. And the change trend was analyzed about hospital and determination method distribution. The judgment criteria, quarterly and annual pass rate, bias and coefficient of variation of the results of the inter-laboratory comparison were analyzed retrospectively, and the results were compared with the results of External Quality Assessment Programme carried out by the National Center for Clinical Laboratories, Shanghai Center for Clinical Laboratories and College of American Pathologists (CAP). The data in the first quarter of 2019 was collected and the imprecision, bias and sigma were calculated, which were drew in the evaluation model of sigma combined with biomedical variation parameters. Results The number of participating laboratories increased from 9 in Shanghai to 192 in the whole country, with an average annual growth rate of 76.6%. The quarterly comparison improved from ±8% to ±6% and the passing rate of participating laboratories increased from 39.1% to nearly 90%. The maximum CV of each instrument among laboratories decreased from 14.3% to 4.8%. In the first quarter of 2019, nearly 60% of the laboratories met 6σcriteria and more than 95% of the laboratories met the standard criteria in the model of biological variation parameters. Conclusion Shanghai Glycohemoglobin harmonization program has improved the harmonization of HbA1c test results among the participating laboratories. Key words: Diabetes mellitus; Glycated hemoglobin A; Reproducibility of results
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".