Laser therapy in the treatment of cicatricial ectropion
Bibliographic record
Abstract
Abstract Introduction The objective of this study was to investigate the clinical effects of laser therapy in treating cicatricial ectropion. Methods Seven patients with cicatricial ectropion were entered into this clinical study. The ectropions were pretreated with the 595‐nm pulsed dye laser if the scar color was red. If there was no red, the UltraPulse fractional CO2 laser was used and parameters were adapted according to the height of scar. MEBT/MEBO was used after laser treatment for wound healing. The degree of ectropion was measured for changes before and after treatment, and the scars were evaluated for changes in melanin, height, vascularity, and pliability using the Vancouver Scar Scale (VSS) before and after treatment. Results All seven patients with cicatricial ectropion entered into this protocol were completely corrected after 1‐2 treatment sessions. The total VSS score, as well as the score for melanin and pliability in 7 patients, showed a decrease following the treatments, and this was statistically significant (P < 0.05). The scores for height and vascularity showed a decrease following the treatments, but there was no significant difference by statistical analysis (P ≥ 0.05). There were no adverse reactions reported. Conclusions The treatment of cicatricial ectropion with laser therapy can not only correct the ectropion, but also improve the scars in the treatment area. Compared with the traditional repair of cicatricial ectropion, the use of fractional CO2 laser provides surgical precision and the advantage of a timely treatment without the need to wait for the scar to stabilize.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".