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Record W2964770711 · doi:10.1002/hed.25889

Tongue reconstruction: Rebuilding mobile three‐dimensional structures from immobile two‐dimensional substrates, a fresh cadaver study

2019· article· en· W2964770711 on OpenAlexaff
Robert M. Baskin, Hadi Seikaly, Raja Sawhney, Deepa Danan, Martha Burt, Sherif Idris, Mohamed Shama, Brian Boyce, Peter T. Dziegielewski

Bibliographic record

VenueHead & Neck · 2019
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTongueCadaverAnatomyTongue NeoplasmMedicineGlossectomyDissection (medical)DentistryOrthodonticsPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the two-dimensional (2D) characteristics of flaps necessary to create three-dimensional (3D) tongue anatomy. METHODS: Dissection of 11 fresh, nonpreserved human cadavers was performed. Six defects in each were created: total tongue, total oral tongue, hemiglossectomy, oral hemiglossectomy, total base of tongue, and hemi-base of tongue. The resections were debulked to create flat, 2D mucosal flaps. The dimensions and shapes of these flaps were determined. RESULTS: Each specimen showed consistent dimensions and geometry between cadavers. The total tongue was pear-shaped, the total oral tongue was egg-shaped, the oral hemi-tongue was bullet-shaped, the hemi-tongue resembled a dagger, the total base of tongue was rectangular, and the hemi-base of tongue was hour-glass shaped. CONCLUSION: Typical dimensions and shapes of common tongue defects were determined. It is conceivable that customizing reconstructive flaps based on these data will increase the accuracy of neo-tongue reconstruction, and thus, improve functional outcomes.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.279
Teacher spread0.265 · 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.

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

Citations8
Published2019
Admission routes1
Has abstractyes

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