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Record W4210976025 · doi:10.1055/s-0041-1742199

Cartilage Chips in Rhinoplasty

2022· article· en· W4210976025 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFacial Plastic Surgery · 2022
Typearticle
Languageen
FieldMedicine
TopicNasal Surgery and Airway Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineRhinoplastyCartilageSurgeryAnatomyNose

Abstract

fetched live from OpenAlex

OBJECTIVE: This study reveals that the cartilage chips can be a good solution for camouflage and augmentation in rhinoplasty. METHODS: In this study, 64 patients who had undergone rhinoplasty from 2014 to 2019 were retrospectively studied. The average age was 31. Forty-nine patients had primary and 15 revision rhinoplasties. Cartilage chips were cut into less than 0.5-mm thickness dimensions changing from 2 to 10 mm. They were used to fill deep radix, depressions at the key area, supratip area, around the grafts to prevent their visibility at the tip. In addition, they were used in the fascia for augmentation. The cartilage chips were sculpted from the septal cartilage in 47, rib in 16, and ear cartilage in one case. RESULTS: They were applied on the radix in 25, middle vault in 37, supratip area in 32, and on the tip in 12 cases. In 30 cases, cartilage chips were mixed with cartilage dust for better fixation and camouflage. They were placed in the fascia in three cases for dorsal augmentation. Complications were seen in three cases in the form of irregularities. CONCLUSION: Cartilage chips are found to be a powerful solution in terms of camouflage and augmentation.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.027
GPT teacher head0.242
Teacher spread0.215 · 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