Feasibility of calcium hydroxyapatite (Radiesse®) for improving the biomechanical properties of facial burn scars: A pilot study
Bibliographic record
Abstract
Background: Effective treatments for facial burn scars remain limited, emphasizing the need for innovative therapeutic approaches. This study explored the feasibility of the use of calcium hydroxyapatite (CaHA, Radiesse®) as a treatment to improve the biomechanical properties of facial burn scars. Objective: To evaluate the potential effects of CaHA injections on the biomechanical properties of facial burn scars and to compare these effects with those of untreated skin. Methods: A prospective longitudinal feasibility study was conducted with 13 patients who had mature facial scars (2-5 years) covering more than 90% of the face, including hypertrophic, atrophic, and/or keloid scars. The forehead, cheek, and jaw areas were measured before treatment (baseline control) and at 2, 4, and 6 months after CaHA application, resulting in 312 measurements. CaHA was injected subdermally on one side of the face, with the opposite side serving as a control. Biomechanical properties were assessed via a Cutometer MPA 580 alongside clinical assessments, photography, and validated scar scales (Vancouver Scar Scale and Patient and Observer Scar Assessment Scale). Results: Preliminary findings suggest that CaHA injections may improve skin extensibility, elasticity, viscoelasticity, hydration, erythema, and pigmentation in the forehead, cheek, and jaw areas. These observations were supported by visual assessments and scale evaluations. Conclusion: This feasibility study indicated that subdermal CaHA injections have potential as a noninvasive approach for improving the biomechanical properties of facial burn scars. However, further studies with larger sample sizes and long-term follow-up are needed to confirm these findings.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".