MétaCan
Menu
Back to cohort
Record W2932028399 · doi:10.1186/s40463-019-0334-y

Free versus pedicled flaps for reconstruction of head and neck cancer defects: A systematic review

2019· review· en· W2932028399 on OpenAlexaff
Fanny Gabrysz‐Forget, Paul Tabet, Akram Rahal, Éric Bissada, Apostolos Christopoulos, Tareck Ayad

Bibliographic record

VenueJournal of Otolaryngology - Head and Neck Surgery · 2019
Typereview
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversité de Montréal
Fundersnot available
KeywordsHead and neck cancerMedicineHead and neckSurgeryRadiation therapy

Abstract

fetched live from OpenAlex

OBJECTIVE: The present review focuses on comparative studies of reconstruction with free flaps (FF) versus pedicled flaps (PF) after oncologic resection. METHOD: A systematic review was developed in compliance with PRISMA guidelines and performed using the Pubmed, Medline, EMBASE, Amed and Biosis databases. RESULTS: A total of 30 articles were included. FF are associated with a longer operative time, a higher cost and a higher incidence of postoperative revisions compared to PF. FF are associated with a longer stay at the intensive care unit than the supraclavicular artery island flap (SCAIF) and with a more extended hospital stay compared to the submental island flap (SMIF). FF are associated with fewer infections and necrosis compared to the pectoralis major myocutaneous flap (PMMF). CONCLUSION: The comparison of both type of flaps is limited by the inherent design of the studies included. In sum, FF seem superior to the PMMF for several outcomes. SMIF and SCAIF compare favorably to FF for some specific indications achieving similar outcomes at a lower cost.

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)0.004
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0080.008
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.053
GPT teacher head0.340
Teacher spread0.288 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations111
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Otolaryngology - Head and Neck SurgerySame topicReconstructive Surgery and Microvascular TechniquesFrench-language works237,207