Follow-Up on Diagnostic Proficiency of Laboratories Equipped To Perform Orthopoxvirus Detection and Quantification by PCR: the Second International External Quality Assurance Study
Bibliographic record
Abstract
Two years after the first external quality assurance study on bioterrorism-relevant viruses, we have conducted a follow-up study on orthopoxvirus detection by PCR. Thirty-three laboratories (27 European, 4 Austral-Asian, and 2 American) participated. Samples contained 0 to 40,000,000 DNA copies of lyophilized monkeypox, cowpox, and vaccinia virus per ml. Laboratories achieved a >80% detection chance above 56,234 copies per ml. Global sensitivity was not significantly improved over that of the first study. Twenty-seven and 9 participants, respectively, were able to genotype and quantify virus. Four of 27 genotyping results were incorrect. Quantification accuracy was significantly better for vaccinia virus than for the other viruses. False-positive results occurred in 22 (11.8%) of all 186 tests on negative samples, but 18 of these were contributed by only five laboratories. Fifty-five percent of laboratories could appropriately detect PCR inhibition. The use of either real-time PCR or commercial diagnostic kits had significant positive influence on laboratory performance.
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 | 0.025 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".