Behind the scenes of EQA – characteristics, capabilities, benefits and assets of external quality assessment (EQA): Part II – EQA cycles
Bibliographic record
Abstract
External quality assessment (EQA) cycles are the smallest complete units within EQA programs that laboratories can use to obtain external assessments of their performance. In each cycle, several samples are distributed to the laboratories registered for participation, and ideally, EQA programs not only cover the examination procedures but also the pre- and post-examination procedures. The properties and concentration range of measurands in individual samples are selected with regard to the intended challenge for the participants so that each sample fulfils its purpose. This aims to ensure the most significant possible information gain in every cycle using the lowest possible number of EQA samples and thus, under economically optimal conditions. Participants examine samples and the results are reported to the EQA provider, who compares them with the target values for individual measurands in every sample. The EQA provider assesses the laboratory performance, and finally communicates the assessment results to the participant. The participants evaluate the outcomes of the assessment of their examination results and can draw conclusions in the case of both failing and passing and, if necessary, define improvement measures. After completion, each cycle is evaluated by the provider so that limitations and weaknesses of the EQA program can be identified and appropriate measures taken, or to confirm its continued suitability and appropriateness.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".