External quality assessment performance in ten countries: an IFCC global laboratory quality project
Bibliographic record
Abstract
OBJECTIVES: This study aimed to assess the validity of external quality assessment (EQA) laboratory results across various cultural and environmental contexts and to identify potential improvement areas. METHODS: The International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Task Force on Global Laboratory Quality (TF-GLQ) conducted a 2-year study (2022 and 2023) in which EQA materials, related software and online training was provided by a commercial vendor to 100 laboratories in ten IFCC member society countries. The results were analysed on a monthly basis by the TF-GLQ, to show the number of submissions per country, tests per lab, acceptability rates, random failures and to get a measure of which analytes performed poorly. RESULTS: The EQA material was dispatched on a quarterly basis. Some countries had problems with customs releasing the material in a timely manner, resulting in laboratories not receiving them on time leading to no submission. We report here the results for the second year of the survey. The number of examinations varied between laboratories, ranging from seven to 84 analytes. Of the ten countries surveyed, six averaged greater than 90 % acceptable results over the whole 12-months cycle, one had unacceptable results for two of the nine months they returned results and the other four were considered to not perform to an acceptable standard. CONCLUSIONS: All 100 participating laboratories indicated satisfaction with the EQA survey and related services, including on-site training, and report handling. However, specimen receiving issues, suggest benefits in dispatching materials for a full 12-month cycle. Significant discrepancies in EQA performance indicate that four countries require long-term assistance, training and guidance. To ensure reliable patient results, promoting EQA in certain countries is essential to achieve the required level of quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.076 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| 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".