Comparison of Artecoll, Restylane and silicone for augmentation rhinoplasty in 378 Chinese patients
Bibliographic record
Abstract
PURPOSE: Dermal fillers have been proven to be safe in soft tissue augmentation; however, their efficacy in modeling the noses of Asian patients has not been demonstrated. METHODS: In this study, 378 patients were included and underwent augmentation rhinoplasty (Artecoll, n=126; Restylane, n=126 and silicone implants, n=126). The subjective and objective outcomes were evaluated on day 1, and months 1, 3, 6, and 12 after injection rhinoplasty. RESULTS: All patients achieved significant improvement in nasal shape and contour immediately after surgery. Patients treated with Restylane failed to maintain the nasal shape and contour 1 year after surgery, whereas patients undergoing Artecoll rhinoplasty completely maintained the post-treatment nasal shape and contour. More patients with silicone implants experienced adverse events and the severity of these events was greater in the silicone group compared to those in the Restylane and Acetoll groups. CONCLUSION: Artecoll rhinoplasty has a low incidence of adverse effects and the shape and contour of the nose are maintained for a prolonged period.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".