MétaCan
Menu
Back to cohort
Record W4309646850 · doi:10.1055/s-0042-1750127

Planes for Perforator/Skin Flap Elevation—Definition, Classification, and Techniques

2022· review· en· W4309646850 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Reconstructive Microsurgery · 2022
Typereview
Languageen
FieldMedicine
TopicReconstructive Facial Surgery Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsElevation (ballistics)MedicinePerforator flapsDebulkingSurgeryAnatomyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Elevation in different layers achieving thin flaps are becoming relatively common practice for perforator flaps. Although postreconstruction debulking achieves pleasing aesthetic results and is widely practiced, customized approach during elevation to achieve the ideal thickness will increase efficiency while achieving the best possible aesthetic outcome. Multiple planes for elevation have been reported along with different techniques but it is quite confusing and may lack correspondence to the innate anatomy of the skin and subcutaneous tissue. METHODS: This article reviews the different planes of elevation and aims to clarify the definition and classification in accordance to anatomy and present the pros and cons of elevation based on the different layers and provide technical tips for elevation. RESULTS: Five different planes of elevation for perforator flaps are identified: subfascial, suprafacial, superthin, ultrathin, and subdermal (pure skin) layers based on experience, literature, and anatomy. CONCLUSION: These planes all have their unique properties and challenges. Understanding the benefits and limits along with the technical aspect will allow the surgeon to better apply the perforator 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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.116
GPT teacher head0.354
Teacher spread0.239 · 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