Factors Influencing Time-to-OR for Urgent Tracheotomy: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: To identify factors influencing time-to-operating room and time-to-airway intervention in urgent tracheotomy and synthesize strategies to reduce delays in airway emergencies. METHODS: A scoping review was conducted following the Joanna Briggs Institute framework and reported according to PRISMA-ScR guidelines. MEDLINE, Embase, Scopus, Web of Science, and Google Scholar were searched from inception to June 2024. Eligible studies involved adults (≥18 years) undergoing urgent tracheotomy for airway obstruction due to malignancy or infection. Trauma, angioedema, laryngotracheal stenosis, post-radiation edema, and vocal fold paralysis were excluded to reduce heterogeneity and focus on institutional/system-level factors. RESULTS: Of 1339 records identified, 3 studies (n = 531 patients) met the inclusion criteria. Dyspnea and stridor were the most common presenting symptoms. Malignancy and deep neck infection accounted for most indications. Reported delays were related to operating room access, staffing shortages, and coordination challenges. Complication rates ranged from 8% to 28%, with hemorrhage and infection most frequent; no deaths were directly attributed to tracheotomy. Decannulation rates were higher in non-malignant than in malignant cases. CONCLUSIONS: Urgent tracheotomy for airway obstruction due to malignancy or infection is time-sensitive, with delays shaped by institutional barriers. Standardized protocols, improved staffing, simulation-based training, and rapid-response teams represent actionable strategies to enhance airway emergency readiness and outcomes.
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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 | 0.016 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".