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Record W3093380811 · doi:10.1177/2292550320963115

The Operation Rainbow Canada Technique for Unilateral Cleft Lip Revision

2020· article· en· W3093380811 on OpenAlexaffabout
Colin White, Hanif Ukani, Kimit Rai

Bibliographic record

VenuePlastic Surgery · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCleft Lip and Palate Research
Canadian institutionsBurnaby Hospital
Fundersnot available
KeywordsRainbowMedicineOrthodonticsComputer scienceOpticsPhysics

Abstract

fetched live from OpenAlex

BACKGROUND: Purpose of this article is to demonstrate the "Operation Rainbow Canada" cleft lip revision technique. This is a surgical technique used by Operation Rainbow Canada on volunteer surgical missions in developing nations. We show how to convert previous Millard or straight line cleft lip repairs to a Fisher anatomic subunit repair, placing a favourable scar along the philtrum. We show a case series of results and explain how this technique gives satisfying aesthetic results for patients seeking unilateral cleft lip revision. METHODS: This technique combines the principles of the anatomic subunit repair for primary cleft lip repair as described by Fisher and the correction of the cleft nose deformity as described by McComb. We apply these 2 techniques to unilateral cleft lip revision at the same operation. RESULTS: Patients for revision unilateral lip and nose deformities were treated with this technique over the course of several international surgical missions. There were over 90 cases of revisions performed by our group on previous repaired cleft lips. These procedures were done in India, China, and Cambodia. CONCLUSION: Previously repaired cleft lips can be improved by our revision procedure. We show how incorporating 2 triangular flaps to lengthen the cleft side of the repaired lip can be done in a revision setting. During lip revision, McCombs sutures can be placed to improve the aesthetic of the nose and correct the nasal alar dome.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.023
GPT teacher head0.259
Teacher spread0.236 · 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 designNot applicable
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 routes2
Has abstractyes

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