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Modified palatal flap via soft palate for skull base reconstruction

2025· article· en· W4409696939 on OpenAlexaff
Rogério Pezato, Aldo Cassol Stamm, Andrea Santos Dumont Costacurta, Carlos Henrique A.B. Baptista, Reginaldo Raimundo Fujita, Camila Dassi, Richard Louis Voegels, Andrew Thamboo, Miguel Soares Tepedino

Bibliographic record

VenueEinstein (São Paulo) · 2025
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Surgical Oncology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSoft palateSkullBase (topology)DentistryOrthodonticsMedicineAnatomySurgeryMathematics

Abstract

fetched live from OpenAlex

OBJECTIVE: Given the importance of vascularized flaps, this study presents a new technique for reconstructing skull defects using a pedicled palatal flap via the soft palate. METHODS: Five preserved cadaveric specimens were used to demonstrate the sequential steps involved in harvesting the palatal flap. In addition, 20 normal paranasal sinus computed tomography scans were analyzed to determine potential measurements of the flap (area, perimeter, greatest anteroposterior distance, and greatest transverse distance). RESULTS: The average flap area in females and males was 11.8cm2 and 12.7cm2, respectively. The average perimeter in females and males was 13.5cm and 14.0cm, respectively. In all cases, the flap reached the infratemporal fossa and lower clivus. CONCLUSION: The palatal flap via the soft palate described in this study proved to be a metrically viable alternative in cadaveric and tomographic studies for use in endonasal surgeries, as well as for skull base and adjacent regions.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.020
GPT teacher head0.295
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations0
Published2025
Admission routes1
Has abstractyes

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