Application of 3D Transparent Facemasks in Long-Term Outpatient Rehabilitation of Facial Scars After Burns: A Retrospective Cohort Study of Improved Appearance of Target Scars With Different Healing Time
Bibliographic record
Abstract
Severe facial burns may cause scarring problems and affect living quality of patients. With the advent of 3D facemasks, it is being used to treat facial scars; however, its efficacy must be confirmed by adequate studies. A retrospective analysis of 26 patients who visited rehabilitation outpatient clinic from 2017 to 2022. Patients were separated into two groups based on the time to healing (TTH) following burn injury: early healing group (TTH ≤ 21 days) and late healing group (TTH > 21 days). To compare treatment outcomes and differences between the two groups, 3D facemask application was assessed using the Vancouver Scar Scale (VSS), patient satisfaction, and complications. In both groups, there were significant improvements in the total VSS scores (P < .01) and each VSS subscore (P < .01). These scar characteristics improved over time as the treatment progressed. Compared with the late healing group, the early healing group had more obvious effects on improving scar pigmentation (P < .05) and vascularity (P < .05) at similar assessment time points after burns. At the last assessment, there was a significant difference in total VSS scores between groups (P = .009). For the early and late healing groups, respectively, the mean gradient value (SE) of the total VSS scores was 1.550 (0.373) and 1.283 (0.224) over the course of the treatment periods. 3D facemasks are effective in the rehabilitation of facial scars caused by burns, which should be used for prevention and treatment in the initial stages of scar development.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".