Fungal infections in patients after recovering from COVID-19: a systematic review
Bibliographic record
Abstract
Background and aims: The presence of fungal infections has been described in patients after recovering from COVID-19. This study aims to conduct a systematic review of studies that reported fungal infections ( Mucor spp., Pneumocystis jirovecii, or Aspergillus spp.) in adults after recovering from COVID-19. Methods: We performed a systematic review through PubMed, Web of Science, OVID-Medline, Embase, and Scopus. The study selection process was performed independently and by at least two authors. We performed a risk of bias assessment using the Newcastle–Ottawa Scale for cohort and case–control studies, and the Joanna Briggs Institute’s Checklists for Case Series and Case Reports. Results: The systematic search found 33 studies meeting all inclusion criteria. There was a total population of 774 participants, ranging from 21 to 87 years. From them, 746 developed a fungal infection. In 19 studies, Mucor spp. was reported as the main mycosis. In 10 studies, P. jirovecii was reported as the main mycosis. In seven studies, Aspergillus spp. was reported as the main mycosis. Regarding the quality assessment, 12 studies were classified as low risk of bias and the remaining studies as high risk of bias. Conclusion: Patients’ clinical presentation and prognosis after recovering from COVID-19 with fungal infection differ from those reported patients with acute COVID-19 infection and those without COVID-19 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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 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".