A Double-Blind, Comparative Study of Nonanimal-Stabilized Hyaluronic Acid versus Human Collagen for Tissue Augmentation of the Dorsal Hands
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Cosmetic surgery to counteract the aging process is an evolving field. Most procedures have concentrated on the face; however, the hands are an often-neglected area. Current methods of hand rejuvenation include autologous fat injection, sclerotherapy, intense pulsed light, chemical peel, and microdermabrasion. Only autologous fat injection restores dermal thinning. We compare the use of hyaluronic acid (Restylane, Medicis Aesthetics Inc.) versus collagen (Cosmoplast, INAMED Aesthetics) for soft tissue augmentation of the dorsal hands. MATERIALS AND METHODS: Ten female patients who demonstrated dermal thinning of the dorsal hands were randomized to receive 1.4 mL of hyaluronic acid or 2.0 cm(3) collagen to alternate interphalangeal spaces of dorsal hands. Patients returned at 1 week, 1 month, 3 months, and 6 months for digital photography and completion of a patient/physician questionnaire. RESULTS: Hands were scored by two separate blinded physicians on scales of 1 to 5 for clearance of veins. Patients scored both tolerability and satisfaction on a scale of 1 to 5. Analysis showed a mean difference of 0.95 (0.004), median difference of 0.9 (0.008) for clearance, and a mean difference of 0.90 (0.010) with a median difference of 1.0 (0.031). The satisfaction difference was not significant with a mean difference of 0.80 (0.070) and median difference of 1.0 (0.117). CONCLUSION: Aging of the hands is a common problem that is often overlooked. The use of soft tissue fillers is a viable tool in hand rejuvenation. In this study hyaluronic acid proved to be superior in efficacy to collagen.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".