Postoperative uvular necrosis: A case series and literature review
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Postoperative uvular necrosis is rare, but can be distressing to the patient when it unexpectedly occurs. Little has been published regarding its predisposing factors and pathophysiology. The purpose of this comprehensive review was to compile cases of postoperative uvular necrosis and identify risk factors and potential causes for this complication. STUDY DESIGN: Retrospective case series. METHODS: The study was performed at an academic tertiary care referral center. Clinical records from four patients treated for postoperative uvular necrosis from 2008 to 2018 were reviewed. A comprehensive literature review was also performed. The MEDLINE, Embase, and Scopus databases were searched, as well as the grey literature. All case reports and literature reviews in the English literature from 1978 to 2018 were systematically identified for review. RESULTS: Four cases of postoperative uvular necrosis diagnosed clinically at our institution were included. The comprehensive literature review identified 26 reports and seven case series, totaling 53 cases of this complication. Use of suction was reported in 19 cases, and six cases reported no use of suction. Ninety-four percent of cases were treated conservatively, whereas 6% underwent excision. Ninety-one percent resolved within 14 days. CONCLUSIONS: Impingement with various devices and vascular trauma from suction each likely play a role in postoperative uvular necrosis. Male oropharyngeal anatomy may be a risk factor, but neither the type of instrumentation nor the type of procedure seem to predict this complication. Proper positioning of the patient and instruments and minimizing suction force help prevent uvular injury. LEVEL OF EVIDENCE: NA Laryngoscope, 130:880-885, 2020.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.003 | 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".