External quality assessment practices in medical laboratories: an IFCC global survey of member societies
Bibliographic record
Abstract
OBJECTIVES: Clinical laboratory results are required for critical medical decisions, underscoring the importance of quality results. As part of total quality management, external quality assessment (EQA) is a vital component to ensure laboratory accuracy. The goal of this survey was to evaluate the current status of global laboratory quality systems and assess the need for implementation, expansion, or harmonization of EQA programs (EQAP) for Clinical Chemistry and Laboratory Medicine. METHODS: The International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Task Force on Global Laboratory Quality (TF-GLQ) conducted a survey of IFCC full and affiliate members (n=110) on laboratory quality practice. A total of 41 (37.3%) countries representing all IFCC regions except North America provided responses about EQA availability and practices. RESULTS: All 41 countries perform EQA, 38 reported that their laboratories had EQA policies and procedures, and 39 further act/evaluate unacceptable EQA results. 39 countries indicated they have international and/or national EQAP and 30 use alternative performance assessments. EQA frequency varied among countries. Generally, an EQAP provided the EQA materials (40/41) with four countries indicating that they did not have an EQAP in their country. CONCLUSIONS: Globally, most laboratories participate in an EQAP and have defined quality procedures for EQA. There remain gaps in EQA material availability and implementation of EQA as a part of a total laboratory quality system. This survey highlights the need for education, training, and harmonization and will guide efforts of the IFCC TF-GLQ in identifying areas for enhancing global laboratory quality practices.
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How this classification was reachedexpand
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 | 0.028 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".