MétaCan
Menu
Back to cohort
Record W4414913754 · doi:10.1097/scs.0000000000011951

Single-Stage Autologous Microtia Reconstruction

2025· article· en· W4414913754 on OpenAlexaff
Dale J. Podolsky, David M. Fisher, Leila Kasrai

Bibliographic record

VenueJournal of Craniofacial Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicReconstructive Facial Surgery Techniques
Canadian institutionsSt Joseph's Health CentreHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMicrotiaCostal cartilageSoft tissueCartilageTissue expansionPlastic surgery

Abstract

fetched live from OpenAlex

Microtia reconstruction using autologous costal cartilage is one of the most challenging procedures in plastic surgery, offering long-term durability and biocompatibility. The Nagata 2-stage autologous technique involves creation of a soft tissue pocket and auricular framework from costal cartilage in the first stage, then projection of the framework in the second stage using a temporoparietal fascial flap and skin graft. However, the second stage compromises the aesthetic result due to skin graft color mismatch, edema, and contracture, while also eliminating the TPF flap as a future salvage option. Consequently, the authors present a single-stage autologous technique for microtia reconstruction with several modifications to the Nagata technique: (1) elevating the framework using a projecting block within a single stage, (2) performing wider undermining of the soft tissue pocket to accommodate this elevation, (3) adding a cranial extension to the tragus for stability, and (4) refining the contour of the inferior crus. In a series of 40 consecutive cases using the single-stage technique, our surgical complication rate was 15%, comparable to rates reported for autologous reconstruction. The single-stage approach yields reliable aesthetic and functional outcomes and maintains future revision options, making it an effective alternative for microtia reconstruction.

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 score0.277
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
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 head0.022
GPT teacher head0.285
Teacher spread0.262 · 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

Explore more

Same venueJournal of Craniofacial SurgerySame topicReconstructive Facial Surgery TechniquesFrench-language works237,207