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Record W4416688166 · doi:10.1177/07488068251397587

How I Do It: The Role of Nasal Filler in Managing Cocaine-Induced Saddle Nose Deformity

2025· article· en· W4416688166 on OpenAlexaff
Brian Rotenberg, Justin Shapiro, Alina Zgardau, Babak Pourmomen

Bibliographic record

VenueThe American Journal of Cosmetic Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicNasal Surgery and Airway Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsCosmesisNoseDeformityFiller (materials)Granulomatosis with polyangiitis

Abstract

fetched live from OpenAlex

Saddle nose deformity is most often the result of intranasal cocaine use, but it may also occur in autoimmune conditions such as granulomatosis with polyangiitis (GPA). Many of these patients are poor candidates for surgical reconstruction because of poor vascularity, ongoing substance use, or large septal perforations, making rib grafting and traditional techniques high risk. In our practice, we have managed this challenging group with injectable filler as a minimally invasive alternative. Between January 2020 and June 2025, 6 patients with cocaine-induced saddle nose deformity were treated in an outpatient setting using Revanesse Sculpt. Outcomes were assessed with standardized pre- and posttreatment photographs and Standardized Cosmesis and Health Nasal Outcomes Survey (SCHNOS) scores obtained at baseline and 3 months. Patients demonstrated visible cosmetic improvement with restored contour and symmetry. All procedures were well tolerated under local anesthesia. To our knowledge, this represents one of the first detailed descriptions of a systematic filler technique for managing saddle nose deformity in this high-risk population. This approach provides a safe, cost-effective, and reproducible method for temporary correction when surgical reconstruction is not feasible.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.272
Teacher spread0.256 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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