Simplifying the injector's armamentarium: An international consensus regarding the use of gel science to differentiate hyaluronic acid fillers and guide treatment recommendations
Bibliographic record
Abstract
BACKGROUND: The Restylane portfolio of soft tissue fillers spans a wide range of indications, due in part to their complementary manufacturing technologies [non-animal stabilized hyaluronic acid (NASHA) and Optimal Balance Technology (OBT/XpresHAn)]. Using an array of products, injectors can achieve a holistic, natural looking effect for their patients. However, with a wide range of products it may be difficult to choose an optimal combination. AIM: Simplify and align global use recommendations for NASHA versus OBT products. METHODS: Two pre-meeting surveys were completed by 11 key opinion leaders with international representation, with the goal of collecting information regarding their current injection practices for various anatomical regions of the face (i.e., temporal region, forehead, tear trough, lateral zygoma, anteromedial cheek, nose, pyriform aperture, nasolabial fold, perioral area, lips, labiomental crease, marionette lines, chin, and jawline). The data collected from these surveys was subsequently discussed in a consensus group meeting involving 11 voting members and 3 nonvoting members. RESULTS: Top product recommendations were identified for each anatomical area, along with secondary and tertiary recommendations that can also be used under defined circumstances. Recommendations were provided based on a consideration of elements such as patient features (e.g., skin thickness, bone structure), the desired aesthetic outcome, experience of the injector, and the preferred injection technique. CONCLUSION: A majority consensus regarding the top NASHA versus OBT product choice for each anatomical region of the face was reached. These recommendations represent international agreement regarding the use of Restylane products.
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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 | 0.142 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.010 | 0.017 |
| 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".