Evaluation of Luminex xTAG Fungal Analyte-Specific Reagents for Rapid Identification of Clinically Relevant Fungi
Bibliographic record
Abstract
Invasive fungal infections (IFI) remain a serious threat to immunocompromised hosts. Current diagnostic methods, including fungal culture and antigen detection, are slow and often lack specificity. Rapid diagnostic tools with increased sensitivity and specificity could improve the care of patients with IFI. Recently, Luminex Molecular Diagnostics (Toronto, Canada) developed 23 analyte-specific reagents (ASRs) for the detection of the most common clinically relevant fungi. This study's objective was to evaluate the sensitivity and specificity of a subset of these ASRs for fungal isolates and clinical specimens. Previously characterized fungal and bacterial isolates (n = 110), blood culture specimens (n = 34), and respiratory specimens (n = 44) were tested using either a Candida 7-plex panel (Candida albicans, Candida glabrata, Candida tropicalis, Candida parapsilosis, Candida lusitaniae, Candida guilliermondii, and Candida krusei) or a mold 11-plex panel (Aspergillus fumigatus, Aspergillus flavus, Aspergillus niger, Aspergillus terreus, Scedosporium prolificans, Scedosporium apiospermum, Fusarium oxysporum/Fusarium solani, Rhizopus arrhizus, Rhizopus microsporus, Mucor indicus, and Cunninghamella bertholletiae). The Candida 7-plex panel correctly identified all Candida isolates as confirmed by fungal culture and biochemical tests, for a sensitivity and specificity of 100%. The mold 11-plex panel correctly identified all mold isolates tested except for A. niger. Fungal isolates of Rhizopus and Mucor species were not detected, either, although they could represent species other than those targeted by the ASRs. Further evaluation will be necessary to confirm the sensitivities of some of the mold ASRs. Implementation of these ASRs will allow same-day detection of fungal DNA in clinical specimens.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".