Tracheal Resection Anastomosis: A Retrospective Analysis of 33 Cases
Bibliographic record
Abstract
ABSTRACT Introduction Laryngotracheal stenosis (LS) is most commonly caused by iatrogenic injury, namely, tracheal intubation. The goal of treatment is the maintenance of a patent airway, which is mostly achieved by surgical intervention. Our objective was to study the effect of perioperative variables on tracheal resection anastomosis (TRA)/cricotracheal resection anastomosis (CTRA) surgical outcomes by identifying statistically significant factors associated with postoperative complications and failure of surgery, i.e., restenosis. Methods Data from the medical records of 33 patients who underwent TRA/CTRA was analyzed by univariate and multivariate logistic regression. The data included perioperative variables such as the etiology of stenosis, comorbidities, and postoperative or long-term complications. Results The study included nine females and 24 males, and most (29, 87.88%) were intubated prior to surgery. Nineteen patients (57.57%) developed one or more postoperative complications, including, but not limited to, surgical site infection and hematoma. Of all patients, six (18.18%) developed long-term restenosis. Multiple factors were significantly associated with the development of postoperative complications. Univariate analysis revealed the following factors as statistically significant: age (p = 0.05), diabetes (p = 0.00001), hypertension (p = 0.00001), and myocardial infarction (p = 0.03). Multivariate analysis showed that age (p = 0.046) and myocardial infarction (p = 0.00001) were independent factors. The study had an overall survival of 97%. Conclusion TRA/CTRA is a complex surgical procedure, and its outcomes can be affected by many factors. More studies with bigger sample sizes are needed to better understand contributing factors and to confirm the already established associations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".