Coronavirus Disease 2019–Associated Invasive Fungal Infection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) can become complicated by secondary invasive fungal infections (IFIs), stemming primarily from severe lung damage and immunologic deficits associated with the virus or immunomodulatory therapy. Other risk factors include poorly controlled diabetes, structural lung disease and/or other comorbidities, and fungal colonization. Opportunistic IFI following severe respiratory viral illness has been increasingly recognized, most notably with severe influenza. There have been many reports of fungal infections associated with COVID-19, initially predominated by pulmonary aspergillosis, but with recent emergence of mucormycosis, candidiasis, and endemic mycoses. These infections can be challenging to diagnose and are associated with poor outcomes. The reported incidence of IFI has varied, often related to heterogeneity in patient populations, surveillance protocols, and definitions used for classification of fungal infections. Herein, we review IFI complicating COVID-19 and address knowledge gaps related to epidemiology, diagnosis, and management of COVID-19-associated fungal infections.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it