Performance of MycAssay Aspergillus DNA real-time PCR assay compared with the galactomannan detection assay for the diagnosis of invasive aspergillosis from serum samples
Bibliographic record
Abstract
Invasive aspergillosis (IA) is a major problem in the immunocompromised population, and its diagnosis is difficult due to the low sensitivity of available tests. Detection of Aspergillus nucleic acid by polymerase chain reaction (PCR) in serum samples is a promising diagnostic tool; however, use of multiple "in-house" methods precludes standardization. The first commercial PCR assay, MycAssay Aspergillus (Myconostica, Ltd), became available recently, and its performance in the diagnosis of IA was evaluated and compared with the galactomannan (GM) assay. Serum samples obtained from patients with hematological cancer were tested retrospectively with MycAssay Aspergillus PCR. Per-episode and per-test analyses were undertaken with 146 sera from 35 hematological patients. Sixteen patients had proven or probable IA and 19 had possible or no IA. In per-episode analysis, MycAssay Aspergillus had a sensitivity of 43.8% (95% confidence interval [CI], 19.8%-70.1%) and a specificity of 63.2% (95% CI, 38.4%-83.7%) for IA diagnosis. In per-test analyses, MycAssay Aspergillus had a lower specificity than the GM assay (83.3% vs. 93.1%, P = 0.04). The addition of PCR to routine clinical practice would have permitted the diagnosis of one additional probable IA in our cohort. Use of PCR instead of GM assay would have delayed the diagnosis in two cases. Aspergillus DNA detection by PCR with serum specimens using MycAssay showed a lower specificity than the GM assay and was associated with a low sensitivity for IA diagnosis. More studies are needed to determine the exact role of MycAssay in IA diagnosis in patients with hematological malignancy.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".