Evaluation of Aspergillus PCR Protocols for Testing Serum Specimens
Bibliographic record
Abstract
A panel of human serum samples spiked with various amounts of Aspergillus fumigatus genomic DNA was distributed to 23 centers within the European Aspergillus PCR Initiative to determine analytical performance of PCR. Information regarding specific methodological components and PCR performance was requested. The information provided was made anonymous, and meta-regression analysis was performed to determine any procedural factors that significantly altered PCR performance. Ninety-seven percent of protocols were able to detect a threshold of 10 genomes/ml on at least one occasion, with 83% of protocols reproducibly detecting this concentration. Sensitivity and specificity were 86.1% and 93.6%, respectively. Positive associations between sensitivity and the use of larger sample volumes, an internal control PCR, and PCR targeting the internal transcribed spacer (ITS) region were shown. Negative associations between sensitivity and the use of larger elution volumes (≥100 μl) and PCR targeting the mitochondrial genes were demonstrated. Most Aspergillus PCR protocols used to test serum generate satisfactory analytical performance. Testing serum requires less standardization, and the specific recommendations shown in this article will only improve performance.
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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 | 0.051 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".