Ultra percutaneous dilation tracheotomy vs mini open tracheotomy. A comparison of tracheal damage in fresh cadaver specimens
Bibliographic record
Abstract
BACKGROUND: To compare the ultra percutaneous dilation tracheostomy (PDT) and mini open techniques (MOT) in randomized fixed and fresh cadavers. Assess degrees of damage to tracheal cartilage and mucosa via tracheal lumen and external dissection. METHOD: Comparative cadaver study was performed, tracheostomy was placed in 36 cadavers (16 fixed, 20 fresh) from July 2004 to December 2004, in University of Alberta, Canada. PDT (size 7) were placed by intensivist and MOT (size 7) otolaryngologist. Both fixed and fresh cadavers were randomized. Evaluation was done according to gender, ease of landmark, mucosal and cartilage injuries. RESULTS: Significant differences in mucosal injury (7 of 9 in UPDT VS 0 of 7 in MOT, p value 0.008), and cartilage injury (8 of 9 in UPDT VS 1 of 7 in MOT p value 0.012) were seen in fixed cadavers; and in fresh cadavers, mucosal injury (5 of 10 in UPDT VS 0 of 10 in MOT, p value 0.043), and cartilage injury (5 of 10 in UPDT VS 0 of 10 in MOT, p value 0.043). CONCLUSIONS: PDT resulted in severe damage to mucosa and cartilage, that might contribute to subglottic stenosis preventing decannulation. Considering the injury, MOT has better outcome than UPDT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".