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Record W4313238265 · doi:10.1002/hed.27290

Neurotization of the radial forearm free flap improves swallowing outcomes in hemiglossectomy defects

2022· article· en· W4313238265 on OpenAlexaff
Emily Marchiano, Lulia A. Kana, Emily L. Bellile, Joshua D. Smith, Keith Casper, Kelly M. Malloy, Steven B. Chinn, Chaz L. Stucken, Mark E. Prince, Douglas B. Chepeha, Andrew J. Rosko, Matthew E. Spector

Bibliographic record

VenueHead & Neck · 2022
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSwallowingMedicineForearmOral cavitySurgeryRetrospective cohort studyMultivariate analysisDentistryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: We examined the effect of free tissue neurotization on speech and swallowing outcomes for patients undergoing reconstruction of hemiglossectomy defects with a radial forearm free flap (RFFF). METHODS: A retrospective study was performed in patients with oral cavity squamous cell carcinoma undergoing a hemiglossectomy and reconstruction with a RFFF. Functional outcomes including nutritional mode, range of liquids and solids, and speech understandability were analyzed 1-year post-treatment. RESULTS: Eighty-four patients were included in this analysis, 41 of whom had neurotized flaps (49%). No significant differences in demographic or clinical variables were seen between the neurotized and non-neurotized groups. On multivariate analysis controlling for BMI, flap area, and N-classification, patients with neurotized flaps were significantly more likely to have normal range of liquids and solids and less likely to have a G-tube. CONCLUSIONS: Neurotization of RFFF reconstructing hemiglossectomy defects results in decreased G-tube dependence and improved range of liquids and solids.

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 score0.020
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.011
GPT teacher head0.250
Teacher spread0.239 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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