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Record W2969908582 · doi:10.1055/s-0039-1692973

Revisiting the Transverse Cervical Artery and Vein for Complex Head and Neck Reconstruction

2019· article· en· W2969908582 on OpenAlexaff
Eitan Prisman, Peter Baxter, Eric M. Genden

Bibliographic record

VenueJournal of Reconstructive Microsurgery Open · 2019
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineSurgeryNeck dissectionAnastomosisHead and neckCervical ArteryFree flapRadiologyChemoradiotherapyHead and neck cancerDissection (medical)VeinRadiation therapyCarcinoma

Abstract

fetched live from OpenAlex

Background Chemoradiotherapy is the primary treatment modality for glottic and pharyngeal subsites. Management of recurrence or second primaries in this setting is a surgical challenge requiring complex free flap reconstruction. One of the major barriers to effective reconstruction is the availability of suitable recipient vessels. We propose that the transverse cervical artery (TCA) is a viable option for complex head and neck reconstruction. Methods A retrospective chart review of 230 consecutive free tissue reconstructive cases was performed by the senior author (EG). Results Forty cases were identified that used the TCA for arterial anastomosis. Twenty-six patients had prior treatment, 13 of which had multimodality treatment. There were no microvasculature free flap failures and 5 minor flap complications. Conclusions Our experience with the TCA suggests it is a viable option for complex head and neck reconstruction, particularly in the setting of prior comprehensive neck dissection or radiation. In addition, the location of the TCA provides favorable pedicle geometry for microvascular anastomosis.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.032
GPT teacher head0.299
Teacher spread0.267 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2019
Admission routes1
Has abstractyes

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