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Record W4406599907 · doi:10.1002/hed.28063

Geometric Study and Clinical Case Series for Mandible Reconstruction With a Single‐Piece Scapular Free Flap

2025· article· en· W4406599907 on OpenAlexafffund
Khanh Linh Tran, Alex Chen, David H. Yang, Jae Young Kwon, Farahna Sabiq, Sidney Fels, Antony J. Hodgson, J. Scott Durham, Eitan Prisman

Bibliographic record

VenueHead & Neck · 2025
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchVancouver Coastal Health Research InstituteTerry Fox Research InstituteMichael Smith Health Research BC
KeywordsScapulaMandible (arthropod mouthpart)Free flapMedicineOrthodonticsDiceSurgeryMathematicsGeometryBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Virtual surgical planning (VSP) with simple cutting templates could help surgeons preoperatively plan scapula reconstructions in the vertical and horizontal orientations. METHODS: Virtually, eight defects were created in ten healthy mandibles and reconstructed with the subject-specific scapula vertically and horizontally. In the clinical series, 15 single-piece scapula mandible reconstructions planned with in-house VSP and guided with simple templates were compared with 15 freehand reconstructions. RESULTS: Virtually, the vertical placement outperformed the horizontal placement in dice score (DSC) and Hausdorff-95 for all but one defect. Clinically, the VSP cohort had shorter operative time (386.6 ± 111.6 min vs. 268.9 ± 50.6 min, p = 0.002), fewer tracheostomies (73% vs. 15%, p = 0.002), lower length of hospital stay (16.6 ± 13.5 days vs. 12.2 ± 8.1 days, p = 0.319), and higher complete/partial union to a non-significant degree (78% vs. 100%, p = 0.471). CONCLUSION: A single-piece scapula free flap is a versatile option for mandibular reconstruction. VSP has time and cost savings potential and quality of life impact that should be further investigated.

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)0.000
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 score0.297
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.031
GPT teacher head0.332
Teacher spread0.301 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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