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Record W2981278596 · doi:10.1002/hed.25990

The infraclavicular pedicled flap in head and neck reconstruction: applications and outcomes

2019· article· en· W2981278596 on OpenAlexaff
Charles Alain, Pierre‐Hugues Fortier, Mathieu Belzile

Bibliographic record

VenueHead & Neck · 2019
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineSurgeryHead and neckComplicationFistulaRetrospective cohort study

Abstract

fetched live from OpenAlex

BACKGROUND: An infraclavicular pedicled flap (ICPF) was recently described in the literature. This anatomical region is attractive for the restoration of head and neck oncological defects. This paper is a review of our experience with this versatile flap. METHODS: A retrospective study was conducted by reviewing the records of all the patients operated in a tertiary-care center between August 2013 and January 2019 whose surgery involved an ICPF. RESULTS: Forty-four patients received an ICPF for various indications, including large vessel coverage in neck/parotid recontouring (34.1%), postlaryngectomy reconstruction (34.1%), and fistula closure (25.0%). All flaps survived. Thirteen patients experienced a postoperative complication (29.5%), six of whom (13.9%) required a repeat procedure under general anesthesia. CONCLUSION: ICPF is suitable for several indications and is a useful adjunctive tool in head and neck reconstruction. It proved to have a high survival rate, with complication rates similar to other regional flaps.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.279
Teacher spread0.270 · 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 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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