Device-based methods in facial skin restoration during rehabilitation after plastic and reconstructive surgeries
Bibliographic record
Abstract
An analysis of current scientific literature shows that device-based treatment methods play an important role in the comprehensive postoperative rehabilitation of patients after reconstructive and plastic surgery on the face. Despite the active introduction of radiofrequency, laser, ultrasound and light methods into clinical practice, the question of comparing their effectiveness remains open. This is particularly relevant in the treatment of the consequences of deep mechanical and thermal damage, which are accompanied by complex processes of skin restoration. Objective: to conduct an analysis, including a multidimensional comparative analysis, of device-based methods for facial skin restoration during rehabilitation after plastic and reconstructive surgery. The study involved 72 subjects (women and men, average age 40.5±8.1 years) who underwent plastic or reconstructive surgery on the face. Depending on the method of hardware exposure, patients were divided into four groups: radiofrequency, laser, ultrasound (HIFU) and light (LED/IPL) therapy. The control group (n=24) consisted of patients who received only traditional treatment without device-based methods. The Vancouver Scar Scale (VSS), biometric parameters (Cutometer®, Corneometer®), dermatoscopy and the visual analogue scale (VAS) were used to evaluate the results. A multidimensional comparative analysis was performed using the sum of squares method, which took into account five key characteristics: clinical effectiveness, duration of the procedure, number of sessions, interval between surgery and the start of therapy, and cost of treatment. LED light therapy received the highest rating (R=1), which was characterised by the shortest interval between surgery and the start of treatment (21 days), the lowest cost (6,000 UAH) and the shortest duration of the procedure (0.5 hours). Radiofrequency and non-ablative laser therapies received the same rating (R=2), indicating their high effectiveness but longer and more expensive therapy (1 hour and 9-15 thousand UAH, respectively). The ultrasound method ranked third (R=3) due to significant time (90 days) and financial (15,000 UAH) constraints, as well as the duration of the procedure (1.5 hours). The results of the study indicate the advisability of early inclusion of LED and radiofrequency therapy in standardised rehabilitation protocols to improve skin healing quality, reduce the risk of scarring and enhance the overall aesthetic effect after plastic and reconstructive surgery on the face.
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How this classification was reachedexpand
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".