Facial soft tissue augmentation with Artecoll <sup>®</sup> : A review of eight years of clinical experience in 153 patients
Bibliographic record
Abstract
OBJECTIVE: Artecoll (Canderm Pharma Inc, Canada) is a semipermanent, injectable, soft tissue filler composed of uniform polymethylmethacrylate microspheres in a bovine collagen gel, which has been used in Europe over the past decade. The authors review their experience using Artecoll as an injectable material for the correction of deep static folds of the face, improvement of nasal asymmetries following rhinoplasty, depressed acne scars and augmentation of the lip. METHOD: A retrospective chart review, subjective patient satisfaction feedback and objective findings noted by the senior author were performed over an eight-year period. A total of 153 patients were treated with Artecoll injections; 74 underwent lip augmentation, 21 underwent deep nasolabial fold augmentation, eight underwent glabellar fold augmentation, 26 were treated for minor nasal dorsal irregularities and 24 were treated for depressed acne scars. RESULTS: No early or delayed allergic responses were reported. Complications occurred most commonly with lip augmentation, in which 13.5% of patients noted significant noticeable bruising postinjection that resolved completely within one week, 51.3% had detectable implant on palpation, and 13.1% required further intervention with massage, steroid injection and/or local excision to correct for lumpiness. Sixty per cent of patients requiring further intervention responded successfully, while local excision was performed on the two patients who failed to respond after six months of massage and steroid therapy. Overall, a total of 11 patients (14.9%) had minor asymmetries or less than optimal results within the lip augmentation study group. Among other sites, the most common complaint was undercorrection of the fold or wrinkle. CONCLUSION: Based on the authors' experience, Artecoll is a safe, viable option for long-term treatment of deep facial wrinkles, nasal asymmetry, hypoplastic or atrophic lips, and depressed acne scars, and the results have been accompanied by a high degree of patient satisfaction. Although the implant is often palpable, rarely does it cause significant visible lumps. Its use and applications as a semipermanent injectable agent certainly warrant further investigation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".