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Use of the Spring Graft for Prevention of Midvault Complications in Rhinoplasty

2006· article· en· W2001515760 on OpenAlexaff
Cenk Ŝen, Deniz İşcen

Bibliographic record

VenuePlastic & Reconstructive Surgery · 2006
Typearticle
Languageen
FieldMedicine
TopicNasal Surgery and Airway Studies
Canadian institutionsCegep de Sept Iles
Fundersnot available
KeywordsMedicineRhinoplastyDorsumSurgeryNoseAnatomyHeading (navigation)ResectionGeology

Abstract

fetched live from OpenAlex

BACKGROUND: Preservation of the middle nasal vault has increasingly become a topic of interest and concern in rhinoplasty. Resection of even a minute amount of roof during hump removal disturbs the stabilizing effect of the upper lateral cartilages, which then causes a fall of upper lateral cartilages medially toward the anterior septal edge, restricting airflow at the internal valves and creating midvault problems. METHODS: The resected alar cartilages were placed deep to the upper lateral cartilages as a strengthened spring to prevent midvault collapse and internal valve incompetency. Although weak and small alar cartilages are limitations of the technique, the authors think that this is not so frequent because the total force exerted on the upper lateral cartilages by the spring graft is higher than the force of each individual two cartilage pieces. RESULTS: It was easy to view the widening of the internal valve and the smooth appearance of the middle third of the nose intraoperatively. All patients were satisfied with the results both functionally and aesthetically. CONCLUSIONS: The spring graft technique, a modification of the splay graft, handles both the functional and aesthetic problems in the dorsal midvault. The spring graft, with its advantages and disadvantages, is a technique that solves the frequently encountered problems of incompetent internal nasal valve and midvault collapse without the need for a second donor site. It is a simple, reliable technique that is easy to learn and execute and must be kept in mind in selected patients.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.323

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 head0.047
GPT teacher head0.264
Teacher spread0.218 · 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

Citations19
Published2006
Admission routes1
Has abstractyes

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