Safety of open airway reconstruction in high-risk post-COVID patients: A case-matched control study
Bibliographic record
Abstract
OBJECTIVES: COVID-19 pneumonia patients may have high rates of intubation and reduced pulmonary function once recovered. Poor pulmonary function is a relative contraindication to open airway reconstruction, post-COVID patients may extrapolate as high-risk for open airway reconstruction. This presents challenges in airway stenosis management of post-COVID pneumonia patients. This study reports the safety and outcomes of open airway reconstruction in carefully selected post-COVID patients. METHODS: A retrospective case-matched control study of six post-COVID tracheal stenosis cases treated with tracheal resection and six matched controls. Controls were matched based on age, length of stenosis, and comorbidities. Primary outcomes are peri-operative safety events and need for future airway interventions. RESULTS: Post-COVID cases and non-COVID controls were closely matched on age, length of stenosis, and comorbidities (p > 0.05). Duration of follow-up was not significantly different (p = 0.1894). No patients required reintubation, tracheostomy, ICU admission, or ventilator placement in the peri-operative period. There was one allergic reaction to antibiotics in a case and one aspiration pneumonia in a control. One case required in-office steroid injection and injection augmentation post-operatively, but no patients from either group required future operative airway intervention during the follow-up period. CONCLUSION: Post-COVID tracheal stenosis cases had comparably excellent outcomes with matched controls after open tracheal construction, with few peri-operative safety events and minimal need for future interventions. These data indicate open airway reconstruction is a safe and effective option for the treatment of tracheal stenosis in carefully selected post-COVID patients.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".