Necrotizing fasciitis of the head and neck in era of COVID-19: a single-institution experience
Bibliographic record
Abstract
Objectives: Necrotizing fasciitis (NF) of the head and neck is a critical condition, known for its severe impact and high mortality rates, often linked with diabetes, odontogenic infections, and immunosuppression. Observations from the University of Ottawa's Department of Otolaryngology - Head and Neck Surgery indicate an increase in NF cases since the COVID-19 pandemic began, suggesting a possible association between COVID-19 and NF. This study aims to assess the incidence of NF since the pandemic's onset and explore its association with COVID-19. Design: Conducted as a single-center retrospective review from January 1, 2015 to April 7, 2023, this study included patients aged over 18 years with histopathologic confirmation of NF, analyzing clinical risk factors, treatment, and outcomes. Patients were divided into pre- and post-COVID-19 groups for comparison. Results: Of 16 patients, 68.7% were in the post-COVID-19 group, with a notable increase in 2022. The most common risk factors were diabetes mellitus (43.8%) and history of odontogenic infection or extraction (31.3%). Only one patient (6.3%) presented with concomitant COVID-19 infection and NF. All patients underwent treatment with serial surgical debridement and intravenous antibiotics with mortality rates rising to 12.5% after the pandemic. Conclusions: Our study demonstrates an increased incidence of NF cases in our institution after the COVID-19 pandemic.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".