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Record W4320484476 · doi:10.1097/prs.0000000000010289

Making the Case for Virtual Surgical Planning: Bilateral Sequential Fibula Flaps with Immediate Dental Implants for Maxillectomy

2023· article· en· W4320484476 on OpenAlexaff
Erin M. Taylor, Joshua Vorstenbosch, Edwin Morrison, Pierce L Janssen, Kenneth L. Kronstadt, Joseph Randazzo, Evan B. Rosen, Peter G. Cordeiro, Farooq Shahzad, Ian Ganly, Evan Matros

Bibliographic record

VenuePlastic & Reconstructive Surgery · 2023
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsMcGill University
FundersNational Cancer InstituteNational Institutes of Health
KeywordsMedicineFibulaMaxillaSurgical planningResectionHead and neckDentistryOrthodonticsSurgery

Abstract

fetched live from OpenAlex

SUMMARY: Oncologic maxillectomy defects requiring bony reconstruction are among the most challenging head and neck cases because of the complex three-dimensional geometry of the midface. Virtual surgical planning technology is advantageous in these cases because it provides superior positional precision and accuracy compared with traditional techniques and facilitates prosthodontic rehabilitation. Maxillary cancer recurrence after an initial fibula flap reconstruction presents a unique challenge. The authors report the first two cases of sequential fibula flaps after second or recurrent cancer of the maxilla. Virtual surgical planning facilitated resection with adequate tumor margins, optimized anatomic positioning of the fibula construct with three-dimensional printed plates, and enabled immediate functional dental implant placement.

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)0.001
metaresearch head score (Gemma)0.002
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: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.048
GPT teacher head0.311
Teacher spread0.263 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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