Behind the scenes of EQA – characteristics, capabilities, benefits and assets of external quality assessment (EQA): Part V – Benefits for stakeholders other than participants
Bibliographic record
Abstract
Abstract External quality assessment (EQA) enhances patient safety through the evaluation of the quality of laboratory-based and point of care testing. Regulatory agencies and accreditation organizations utilize the results and the laboratory’s response to them as part of assessing the laboratory’s fitness to practice. In addition, where EQA samples are commutable and the assigned value has been determined using reference measurement procedures (RMPs), EQA data contributes to the verification of metrological traceability of assays as part of the post-market surveillance of in vitro diagnostic (IVD) medical devices (IVD-MDs). More broadly, the scientific and medical communities use EQA data to demonstrate that medical laboratory examination procedures are fit for clinical purposes, to evaluate common reference intervals, and inclusion of data in clinical databases. Scientific groups, the IVD industry, reference laboratories and National Metrology Institutes can work with EQA providers to identify measurands, which should urgently be supported by the development of reference materials or methods. The ability of health systems to respond effectively to fast-evolving medical challenges, such as the Coronavirus Disease-19 (COVID-19) pandemic, is reliant on EQA to demonstrate confidence in the performance of new laboratory methods and testing services. EQA providers are uniquely positioned to assess the performance of IVD-MDs in addition to individual laboratories and testing sites. Although the primary focus of EQA providers remains the improvement of the performance of individual laboratories, there are many stakeholders who benefit from EQA performance data.
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 | 0.139 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".