MORPHOLOGICAL AND ETIOLOGICAL FEATURES OF FACIAL SKIN SCARS
Bibliographic record
Abstract
Objective. The aim of this study was to investigate the clinical manifestations of skinscarslocalized in dif erent facial zones. Materials and Methods. A total of 165 patients with keloid scars on the facial skinwereexamined. Clinical assessment of scars was performed using the Vancouver scale for hypertrophic and keloid scars, and the classification of D. Goodman (2006) for atrophic scars. The Visual AnalogueScale (VAS) was applied to determine the degree of pain in keloid scars. Results. Scars were most commonly localized in the middle zone of the face (91/165; 55.1%), which was 1.9 times more frequent compared to the upper zone (49/165; 29.7%) and 3.6timesmorefrequent than in the lower zone of the face (25/165; 15.2%). Severe types of scars (hypertrophicandkeloid) occurred 1.3 times more often than atrophic scars (93/165; 56.3%vs. 72/165; 43.6%). Theprevalence of severe scars in the middle zone of the face was 1.7 times higher than in the lowerzoneand 4.7 times higher than in the upper zone. Among 165 patients seeking laser dermabrasion, in55.1% of cases lesions were localized in the middle facial zone, with an area of 10–50 cm² in46.7%of cases. Atrophic scars accounted for 42.9% of all facial scar lesions. The main etiological factorforfacial skin scarring was acne disease. Conclusion. The findings indicate that the localization and morphological type of facial skinscars are closely associated with their etiology and duration. These factors must be takenintoaccount when planning therapeutic and rehabilitation measures as well as in selecting appropriateaesthetic correction strategies. Keywords: atrophic scars, hypertrophic scars, keloid scars, facial zones, treatment methods.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".