The evolving role of hyaluronic acid fillers for facial volume restoration and contouring: a Canadian overview
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it