Percutaneous Tracheostomy With a Demistifier Canopy in the COVID-19 Era: A Safe Technique in the Intensive Care Unit
Bibliographic record
Abstract
BACKGROUND: Endoscopic percutaneous tracheostomy (PT) is a safe technique that is performed frequently by otolaryngologists and intensivists. New challenges have been identified in order to maintain the safety of this procedure during the COVID-19 pandemic. A novel approach, using a modified demistifier canopy, was developed during the first wave of the pandemic and implemented for 17 consecutive percutaneous tracheostomies in order to enhance procedural safety. METHODS: A protocol was developed after performing a literature review of tracheostomy in COVID-19 patients. A multidisciplinary tracheostomy team was established, including the departments of otolaryngology, critical care, and respiratory therapy. Simulation was performed prior to each PT, and postoperative debriefings were done. RESULTS: A protocol and technical description of PT using a modified demistifier canopy covering was written and video documented. Data were collected on 17 patients who underwent this procedure safely in our tertiary care hospital. There were no procedure-related complications, and no evidence of COVID-19 transmission to any member of the health care team during the study period. CONCLUSION: As patients continue to recover from COVID-19, their need for tracheostomy will increase. The technique described provides a safe, multidisciplinary method of performing PT in COVID-19 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".