The evolving role of hyaluronic acid fillers for facial volume restoration and contouring: a Canadian overview
Bibliographic record
Abstract
Recent advancements, including more versatile facial fillers, refined injection techniques and the adoption of a global facial approach, have contributed to improved patient outcome and increased patient satisfaction. Nine Canadian specialists (eight dermatologists, one plastic surgeon) collaborated to develop an overview on volume restoration and contouring based on published literature and their collective clinical experience. The specialists concurred that optimal results in volume restoration and contouring depend on correcting deficiencies at various layers of the facial envelope. This includes creating a foundation for deep structural support in the supraperiosteal or submuscular plane; volume repletion of subcutaneous fat compartments; and the reestablishment of dermal and subdermal support to minimize cutaneous rhytids, grooves and furrows. It was also agreed that volume restoration and contouring using a global facial approach is essential to create a natural, youthful appearance in facial aesthetics. A comprehensive non-surgical approach should therefore incorporate combining fillers such as high-viscosity, low-molecular-weight hyaluronic acid (LMWHA) for structural support and hyaluronic acid (HA) for lines, grooves and furrows with neuromodulators, lasers and energy devices.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".