Ensuring diagnostic testing accuracy for patient care and public health- COVID-19 testing scale-up from an EQA provider’s perspective
Bibliographic record
Abstract
In response to the coronavirus pandemic (COVID-19) and scale up of diagnostic testing, the Canadian Microbiology Proficiency Testing program created a new proficiency testing (PT) program for the molecular and antigen detection of SARS-CoV-2. The program was geared to point of care testing (POCT) sites located in each of the eight provincial Health Authorities across British Columbia, Canada, with the intention to monitor testing quality. The PT program consisted of 6 shipments in a year, each containing a set of 4 samples either positive for SARS-CoV-2 virus or negative. The program began with initial 23 sites enrolling in March 2021, expanding to >100 participants by December 2021. After the first two surveys, it was observed that testing performance (accuracy) was consistently acceptable for sites using nucleic acid technology (NAT), however performance by sites using rapid antigen detection (RAD) methods was poor, especially when testing the weakly positive samples. A root cause investigation of poor testing performance revealed gaps in the execution of testing methods and also in results interpretation. These quality issues were most commonly associated with new testers who lacked experience with diagnostic testing. Tester training and mentoring was reinforced as was retraining of personnel; sample processing instructions were modified, and a training video was also created for testing sites. As a result of these interventions, sites improved their testing accuracy and the performance of POCT sites using RAD methods came to more closely match the performance of sites utilizing NAT. Overall, the PT program was highly successfully and improved quality of testing in the province. This work demonstrates the critical value of an external quality assessment (EQA) partner towards improving patient and public health and safety, especially when testing is conducted outside of an accredited medical laboratory setting.
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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 | 0.097 | 0.149 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".