Translaryngeal Tracheostomy Needle Introducer: a simple device to improve safety and reduce complications during Fantoni’s translaryngeal tracheostomy procedure: trial on human cadavers
Bibliographic record
Abstract
BACKGROUND: Percutaneous dilatational tracheostomy (PDT) is the most frequently performed procedure in patients requiring prolonged mechanical ventilation. A crucial step in such procedures is needle insertion into the trachea. To simplify this procedure and increase its safety, we developed a new device, the translaryngeal Tracheostomy Needle Introducer (tTNI), for use with Fantoni's method. This cadaver study was designed to assess the performance of the tTNI on human anatomy. METHODS: We tested the tTNI in a cadaver laboratory; the operators included two experts trained in PDT and three without specific training in the procedure. We performed 58 needle insertion attempts on 13 cadavers. We compared the tTNI technique with the standard needle insertion approach using external landmarks. We recorded the number of attempts needed to optimise needle insertion, time required in seconds, final position of the needle and complications related to needle insertion. RESULTS: tTNI use resulted in fewer puncture attempts (1.91 ± 1.34 vs. 1.19 ± 0.5, p < 0.001), less time (36.8 ± 51.6 s vs. 13.14 ± 15.57 s, p < 0,001) and increased precision on the first puncture (18.87 ± 25.38° vs. 7.5 ± 12.95°, p < 0,005). We did not observe any complication with tTNI use, whereas complications found using the standard method were in line with the literature. CONCLUSIONS: The tTNI is a device that simplifies needle insertion by enhancing the accuracy of insertion with fewer attempts and higher precision, even when used by less experienced operators. Clinical testing is required to evaluate the device performance in 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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".