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Record W3008634734 · doi:10.1177/1203475420902044

Gaining Large Coverage With Small Island Pedicle Flaps in Tight Cosmetic Subunits: Taking Advantage of Rotation and Pincer Principles on the Nasal Ala

2020· article· en· W3008634734 on OpenAlexaff
Noelle Wong, Irèn Kossintseva

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2020
Typearticle
Languageen
FieldMedicine
TopicReconstructive Facial Surgery Techniques
Canadian institutionsUniversity of British ColumbiaDalhousie University
Fundersnot available
KeywordsPincer movementMedicineRotation flapRotation (mathematics)SurgeryComputer scienceArtificial intelligenceBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Island pedicle flaps (IPFs) are widely used in reconstructive surgery due to their versatility, tissue efficiency, and excellent clinical outcomes. While IPF rotations and 'pincer flap' modifications have previously been sparsely described, they are not often discussed in the literature. OBJECTIVE: We demonstrate the use of both rotating IPFs and pincer techniques for defects traditionally considered too large for classic IPF design on the nasal ala. METHODS: Forty-four patients underwent alar repair using the rotation or combined rotation with pincer modification to the standard IPF technique from August 2014 to May 2017. Our technique is described and case examples are presented with photographs. RESULTS: Forty-four patients with an average alar defect size of 1.2 cm underwent repair using rotation only or rotation with pincer modification of the classic IPF approach. CONCLUSIONS: Reconstruction of large defects in small facial cosmetic subunits such as the nasal ala can be performed using principles of both rotating IPFs and the 'pincer flap' technique. The degree of rotation is directly related to the length of coverage. Modifications to the flap are straightforward to perform. Using these techniques, larger defects that previously would have been reconstructed using flaps that cross cosmetic boundaries can now be reconstructed within the same cosmetic unit, thus, improving aesthetic outcomes.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

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

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 head0.055
GPT teacher head0.277
Teacher spread0.222 · 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 teacher head, 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

Citations1
Published2020
Admission routes1
Has abstractyes

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