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Record W2467809042 · doi:10.1055/s-0036-1579630

Modified Facial Artery Musculomucosal Flap for Reconstruction of Posterior Skull Base Defects

2016· article· en· W2467809042 on OpenAlexaff
Liyue Xie, François Lavigne, Tareck Ayad, Philippe Lavigne

Bibliographic record

VenueJournal of Neurological Surgery Reports · 2016
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Surgical Oncology
Canadian institutionsUniversité de MontréalHôpital Notre-Dame
Fundersnot available
KeywordsMedicineSkullAnatomySurgeryFasciaOral cavityDentistry

Abstract

fetched live from OpenAlex

Objectives The superiorly pedicled facial artery musculomucosal (FAMM) flap has been successfully used for reconstruction of head and neck defects since 1992. Common sites of defects include the oral cavity and oropharynx. This article presents a clinical case in which we have successfully used a newly developed modification of the FAMM flap for bulky nasopharyngeal and skull base reconstruction. Results Our patient is a 71-year-old man who presented with a large parapharyngeal and clival chordoma. After tumor removal through combined endoscopic and cervical approach, the internal carotid artery (ICA) in the nasopharyngeal portion was left exposed. A modified superiorly based FAMM flap measuring up to 10 cm in length and 2.5 cm in width was successfully harvested and used to completely cover the defect and the ICA. The flap survived local radiation therapy at the long-term follow-up. Conclusion We have developed a new modification of the FAMM flap, using the fascia of the masseter muscle. This is the first reported case in the literature using a modified FAMM flap for the reconstruction of nasopharyngeal and skull base defect.

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.283
Teacher spread0.240 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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