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Record W4415785286 · doi:10.14639/0392-100x-n3138

Stapler-assisted total laryngectomy and hybrid primary puncture: analysis of functional results

2025· article· en· W4415785286 on OpenAlexaff
Claudio Parrilla, Giorgia Rossi, Ylenia Longobardi, Luca Perna, Mario Rigante, Maria Clara Pacilli, Lucia D’Alatri, Jacopo Galli

Bibliographic record

VenueActa Otorhinolaryngologica Italica · 2025
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsUniversity Hospital Foundation
Fundersnot available
KeywordsLaryngectomyOtologyPrimary (astronomy)Laryngeal NeoplasmNeurotology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.253
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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