Rhinocerebral Mucormycosis and COVID-19 Pneumonia
Bibliographic record
Abstract
As the coronavirus disease 2019 (COVID-19) pandemic is evolving, more complications associated with COVID-19 are emerging. In this case report, we present a case of rhinocerebral mucormycosis concurrent with COVID-19 pneumonia in a 41-year-old man with a history of type 1 diabetes mellitus (T1DM). COVID-19 pneumonia was diagnosed with reverse transcription-polymerase chain reaction (RT-PCR). He was promptly treated with steroids and hydroxychloroquine, as this was the recommended regional COVID-19 practice patterns at the time. He was treated with intravenous (IV) fluids and an insulin drip for his diabetic ketoacidosis (DKA), cefepime and IV abelcet, along with three surgical debridements for the rhinocerebral mucormycosis. The pneumonia resolved during the course of his stay in the hospital. With prompt diagnosis and treatment of rhinocerebral mucormycosis, the patient was cleared for discharge and was instructed to complete his course of treatment with coumadin and IV abelcet at home. Saprophytic fungi cause rhinocerebral mucormycosis, a rare opportunistic infection of the sinuses, nasal passages, oral cavity and brain. It usually occurs in patients with poorly controlled diabetes mellitus or those who are immunocompromised, which is again demonstrated in this case report. In the setting of COVID-19 pneumonia and an underlying condition, healthcare professionals should act promptly. In cases where mucormycosis infection is suspected, a prompt diagnosis and treatment should be started because of the angioinvasive character and rapid disease progression that contribute to the severity of the mucormycosis 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".