Stapler-assisted total laryngectomy and hybrid primary puncture: analysis of functional results
Bibliographic record
Abstract
Objective. To describe a new surgical technique called Hybrid Primary Puncture in Stapler-assisted Total Laryngectomy and to retrospectively compare two groups of patients undergoing total laryngectomy (TL) with or without the use of stapler. Methods. Retrospective analysis performed on 110 patients undergoing TL and voice prosthesis (VP) primary placement, divided into “stapler group” and “no-stapler group”. The two groups were compared in terms of pharyngocutaneous fistula (PCF) incidence and events related to voice rehabilitation (hypertonicity, voice quality, complications related to VP). Results. No statistically significant difference (p > 0.05) was found in “stapler” and “no-stapler” groups analysing the incidence of PCF, the percentages of patients who developed hypertonicity and experienced complications in the use and management of the VP. Regarding the vocal quality, a Quality of Voice index of “good” was found in similar percentages of patients in the two groups. Stratifying the sample, no significant differences emerged between “stapler” vs “no-stapler” in salvage TL in any of the parameters investigated. Conclusions. The new technique allows to exploit, without any additional risk, the potential of stapler and primary VP placement.
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".