Serum antigen tests for the diagnosis of invasive aspergillosis: a retrospective comparison of five <i>Aspergillus</i> antigen assays and one beta-D-glucan assay
Bibliographic record
Abstract
ABSTRACT Invasive aspergillosis (IA) is a life-threatening infection. Early and specific diagnosis is pivotal to ensure adequate therapy. Antigen testing from blood is a widespread and convenient diagnostic approach. Various tests for the detection of Aspergillus antigen as well as for the panfungal antigen β-1,3-D-glucan (BDG) are available, for which comprehensive comparisons are still lacking. Blood samples of 82 proven/probable (11/71) IA patients and 52 controls were tested using two enzyme-linked immunosorbent assays (ELISAs) (Bio-Rad and Euroimmun), one chemiluminescent immunoassay (CLIA) (Vircell), one BDG assay (Fujifilm Wako), and two point of care (PoC) assays (Immy sōna and OLM). PoC assays were evaluated visually and used automated read out systems. Of the 82 IA patients, 37 had received solid organ transplantation (SOT) and 25 hematopoietic stem cell transplant (HSCT). Sensitivities and specificities for the eight test systems ranged from 27% to 71% and from 64% to 100%. Estimating a 10% prevalence of IA, test performance would have resulted in positive and negative predictive values of 14%−100% and 91%−95%. Areas under the curve (AUCs) for all tests except GM were below 0.7. When the cut-off values for quantitative tests were normalized to a specificity close to 95%, sensitivities ranged from 14% to 40%. The use of automated read out systems for the PoC assays had a significant impact. Combining different tests did not result in better test strategies. Sensitivity of Aspergillus antigen testing from single serum samples is low. Due to specificity issues, the majority of tests is not suited for screening purposes. The different assays can meet different needs in different diagnostic settings.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".