Hypertrophic Scar Following Excisional Surgery and Full-Thickness Skin Grafting Due to Rhinophyma Treated with 1064 nm Q-Switched Neodymium:Yttrium Aluminum Garnet Laser
Bibliographic record
Abstract
Rhinophyma is characterized by progressive enlargement of the nasal skin, which is considered to be an advanced stage of phymatous rosacea. Esthetic disfigurement makes surgical treatment necessary for this condition. Hypertrophic scars are the consequence of alterations in the skin's healing process following surgical interventions. Laser may be the treatment of choice in hypertrophic scars. We reported a case of hypertrophic scars following excisional surgery and full-thickness skin grafting due to rhinophyma in an 18-year-old male who was consulted from the Department of Plastic Surgery and Reconstruction. The 1064 nanometer (nm) Q-switched Neodymium: Yttrium Aluminum Garnet (QS Nd:YAG) with 4 mm spot size, 1.5 J/cm2 and 1 Hz was applied to the hypertrophic scars for three sessions within one month interval. Clinical improvement was observed as indicated by the patient's Vancouver scar scale score and spectrophotometry result, and no side effects were found. Nd:YAG laser is a non-ablative device that targets hemoglobin, water, and melanin. Any thermal effects on dermal tissue containing blood vessels could result in reduced blood flow through the capillaries in the dermal papillae. QS Nd:YAG-induced selective photothermolysis was responsible for collagen breakdown and reduced collagen production in hypertrophic scars. The 1064 nm QS Nd:YAG laser gave good results in this case although more treatment sessions may be recommended and a longer follow-up is necessary in order to assess the stability of the result.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".