Behind the scenes of EQA – characteristics, capabilities, benefits and assets of external quality assessment (EQA): Part I – EQA in general and EQA programs in particular
Bibliographic record
Abstract
Abstract This is the first in a series of five papers that detail the role and substantial impact that external quality assessment (EQA) and their providers‘ services play in ensuring in-vitro diagnostic (IVD) performance quality. The aim is to give readers and users of EQA services an insight into the processes in EQA, explain to them what happens before EQA samples are delivered and after examination results are submitted to the provider, how they are assessed, what benefits participants can expect, but also who are stakeholders other than participants and what significance do EQA data and assessment results have for them. This first paper presents the history of EQA, insights into legal, financing and ethical matters, information technology used in EQA, structure and lifecycle of EQA programs, frequency and intensity of challenges, and unique requirements of extra-examination and educational EQA programs.
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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 | 0.024 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".