A Double-Blind, Comparative Study of Nonanimal-Stabilized Hyaluronic Acid versus Human Collagen for Tissue Augmentation of the Dorsal Hands
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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