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Record W3216435852 · doi:10.1186/s40463-021-00523-z

Reconstruction of medium-size defects of the oral cavity: Radial forearm free flap vs facial artery musculo-mucosal flap

2021· article· en· W3216435852 on OpenAlexaff
Badr Ibrahim, Akram Rahal, Éric Bissada, Apostolos Christopoulos, Louis Guertin, Tareck Ayad

Bibliographic record

VenueJournal of Otolaryngology - Head and Neck Surgery · 2021
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversité de MontréalHôpital du Sacré-Cœur de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsOral cavitySurgeryMedicineComplicationDentistry

Abstract

fetched live from OpenAlex

BACKGROUND: The radial forearm free flap (RFFF) is the most commonly used flap for defects of the oral cavity. The facial artery musculomucosal (FAMM) is a safe and effective method to reconstruct medium sized defects of the oral cavity. No comparison exists between the FAMM flap and RFFF. METHODS: 1) Retrospective chart review from 2007 to 2016. 2) Cost difference analysis. RESULTS: Thirteen FAMM flap cases and 18 RFFF met inclusion criteria. The FAMM flap showed a tendency to lower rates of return to the operating room (p = 0.065) as well as lower rates of complications not requiring return to the OR with 1 complication in 1 patient as opposed to 10 patients with 15 complications (p = 0.008). Also, FAMM flap had shorter operative times compared to the RFFF group (7.2HR and 8.9 HR respectively, p = 0.002). The average operative room related costs for a FAMM flap were 6510 CAD vs 10,703 CAD for RFFF (p < 0.0005). Speech and swallowing outcomes were similar (p > 0.05). CONCLUSION: The FAMM flap can be used for reconstruction of medium-size defects of the oral cavity with functional outcomes similar to the RFFF while decreasing the associated costs and morbidity.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.089
Threshold uncertainty score0.685

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.251
Teacher spread0.236 · 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

Citations23
Published2021
Admission routes1
Has abstractyes

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