Fatal Necrotizing Fasciitis Mediated by <i>Escherichia coli</i> After Parotidectomy and Neck Dissection: A Case Report and Review of the Literature
Bibliographic record
Abstract
Necrotizing soft tissue infection (NSTI) is a rare, but life-threatening, complication of head and neck surgery. We present a 70-year-old male with a history of immunosuppression who presented with polymicrobial NSTI following parotidectomy and neck dissection for cutaneous squamous cell carcinoma. The objective of this report was to promote awareness for NSTI following parotidectomy and selective neck dissection and highlight the management measures that can optimize survival outcomes. We performed a database search that identified 1,025 citations, of which 5 articles described classified as craniocervical necrotizing fasciitis following major head and neck surgery. Consent was obtained from the patient for inclusion in the research study and Institutional Review Board approval was waived. Our literature review yielded 6 cases of craniocervical necrotizing fasciitis following major head and neck surgery. This NSTI, however—unlike the others previously reported—was predominantly mediated by Escherichia coli , a bacterium associated with elevated mortality rates. Despite immediate awake fiberoptic intubation, repeated surgical debridement, and empirical antibiotic therapy, he deteriorated rapidly and was withdrawn from life-support on postoperative day seven. Prophylactic antibiotics, airway management, prompt diagnosis, and surgical debridement are critical for limiting mortality in NSTI of the head and neck.
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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 | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".