The use of biomarkers and molecular methods for the earlier diagnosis of invasive aspergillosis in immunocompromised patients
Bibliographic record
Abstract
Invasive aspergillosis (IA) is an opportunistic infection that is often life threatening in the immunocompromised host. Early diagnosis is critical, especially given the efficacy and availability of several new anti-fungal therapies. Current (2008) diagnostic criteria have limited ability to detect early infection and are aimed at establishing disease. Although histopathology and culture techniques have traditionally been used to make a proven diagnosis of IA, their dependence on tissue samples and slow turnaround times hamper early confirmation of IA. Serologic detection of circulating galactomannan and 1,3-β-D-glucan fungal biomarkers show promise for improving the diagnosis of IA, and their use is included in the EORTC/MSG diagnostic criteria for IA. Numerous studies have evaluated the diagnostic performance of these two biomarkers and shown that they have suboptimal sensitivity when used alone for early diagnosis of proven IA. Currently available molecular assays also suffer from a lack of standardization. Evaluation of the use of different combinations of test methods to enhance diagnostic accuracy is also being done but prompt, accurate diagnosis of IA remains a clinical and diagnostic challenge. The clinical validity and limitations of biomarkers and current molecular methods for the early diagnosis of IA are summarized in this review with respect to the different patient populations at risk for this serious infection.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".