Bibliographic record
Abstract
calibration of secondary standards which are commonly used as run controls or in quality assurance proficiency panels.It is difficult to cross-reference molecular assays or to validate novel NAT assays without well characterized reference materials.The lack of an IS for West Nile virus (WNV) RNA, for example, hindered the determination of the analytical sensitivity (95% limit of detection).Therefore, the German authority PEI required the use of the Health Canada WNV Reference Reagent or similarly calibrated secondary standards for the quantification of assays.These standards are expressed in copies/ml and make comparison of results more difficult.In addition to the standardization of NAT by the use of IS, the periodic review of the actual performance of methods is the second main pillar of quality assurance and quality control.Moreover, participation in external quality assurance programs is necessary.Several programs for proficiency testing for infectious diseases have been established in molecular diagnostics in Europe, including EQUALqual, a project proposed under the auspices of the European Communities Confederation of Clinical Chemistry and Laboratory Medicine (EC4) and funded by the European Commission, and several commercially available programs provided by, for example, Quality Control for Molecular Diagnostics (QCMD, UK), the United Kingdom National External Quality Assessment Service (NEQAS, UK), the Reference Institute for Bioanalytics (RfB, Germany), and the Institute for Standardization and Documentation in Medical Laboratory (INSTAND, Germany) [5].Recently, a novel format for a personalized external quality assurance program (EQAP) was described that provides a collaborative trial for genomic detection of eight viruses simultaneously: HAV, HBV, HCV, HIV-1, HIV-2, parvovirus B19 (B19V), WNV and human cytomegalovirus (HCMV) [6].The so-called MultiVir NAT is organized by the RfB, Germany.This EQAP enables flexible participation regarding the testing of 1-8 different transfusion-relevant viruses, qualitatively and/or quantitatively, and provides result reporting within 4 weeks.Barcoded samples can be processed as routine samples in an automated PCR workflow.
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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 | 0.091 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".