Diagnosis and management of invasive fungal diseases by next-generation sequencing: are we there yet?
Bibliographic record
Abstract
INTRODUCTION: Invasive fungal diseases (IFDs) are a serious threat to immunocompromised patients. Routine diagnostic methods have limited performance in identifying IFDs. Next-generation sequencing (NGS), including metagenomic NGS (mNGS) and whole-genome sequencing (WGS), recently emerged as diagnostic methods that could provide more accurate and timely diagnoses and management of IFDs. AREAS COVERED: This article describes the emergence of NGS as a diagnostic tool to address the limitations of current tests. The literature regarding its application and clinical utility in the diagnosis of IFDs is reviewed. Practical considerations, challenges, and opportunities as they relate to the development and implementation of mNGS and WGS for fungal pathogens are discussed. EXPERT OPINION: NGS emerged over a decade ago with the potential to solve many of the challenges in diagnosing infectious diseases, including IFDs. However, published literature has yielded conflicting data about its clinical utility. The increased clinical adoption of NGS is improving our understanding of how to interpret and use its results to guide actionable decisions. Still, several gaps remain. As the cost, effort, and expertise involved in performing NGS decrease and the reporting of its results becomes standardized, NGS is poised to fill current gaps in the diagnosis of IFDs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".