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Record W4411682547 · doi:10.1016/j.wneu.2025.124217

Novel Vascularized Pedicle Flaps for Dural Reconstruction via Endoscopic Transorbital Approach: Temporalis Muscle and Pericranial Flaps

2025· article· en· W4411682547 on OpenAlexaff
Marcos Ezequiel Yasuda, Mohd Afiq Mohd Slim, Yousif Al‐Ammar, Doron D. Sommer, Matteo de Notaris, Kesava Reddy

Bibliographic record

VenueWorld Neurosurgery · 2025
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Surgical Oncology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineSurgeryTemporalis muscle

Abstract

fetched live from OpenAlex

BACKGROUND: The endoscopic transorbital approach (ETOA) has emerged as a valuable minimally invasive technique in skull base surgery, providing direct access to the anterior and middle cranial fossae. However, effective reconstruction of dural defects remains a significant technical challenge. This study evaluates the feasibility and anatomical characteristics of temporalis muscle flaps (TFs) and pericranial flaps (PFs) for dural reconstruction following ETOA, using cadaveric models. METHODS: Four fresh cadaveric heads were dissected. The TFs were harvested using an inferior-based pedicle, whereas the PFs were obtained endoscopically. Flap dimensions were measured and reported as median and range. RESULTS: The TFs demonstrated consistent dimensions, with a median caudal length of 20.75 mm (range 20-22) and a median lateral width of 21.25 mm (range 20-23). PFs exhibited greater variability, with a mean length of 97.75 mm and a mean width of 54 mm. These findings suggest that TFs are optimal for defects centered on the temporal dura, whereas PFs offer broader, customizable coverage for larger or more complex reconstructions. CONCLUSIONS: Both TFs and PFs are feasible options for dural reconstruction following ETOA, providing robust vascularization and adaptability to defect size and location. Further clinical studies are warranted to validate their application in live surgical settings.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.023
GPT teacher head0.272
Teacher spread0.249 · 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 designBench or experimental
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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