Focal Treatment of Acne Scars With Trichloroacetic Acid: Chemical Reconstruction of Skin Scars Method
Bibliographic record
Abstract
BACKGROUND: Acne scarring is a common complication of acne and yet no appropriate and effective single treatment modality has been developed. We suggest a technique consisting of the focal application of higher trichloroacetic acid (TCA) concentrations by pressing hard on the entire depressed area of atrophic acne scars. This technique is called chemical reconstruction of skin scars (CROSS) by the authors. OBJECTIVE: To evaluate the clinical effects of CROSS on atrophic acne scars in dark-complexioned patients. METHODS: An analysis was conducted of 65 patients with atrophic acne scars who were treated with CROSS in our hospitals between July 1996 and July 2001. Thirty-three patients were treated with 65% TCA CROSS and 32 patients were treated with 100% TCA CROSS. All patients had Fitzpatrick skin types IV-V. RESULTS: Patient treatment data indicated that 27 of 33 patients (82%) (the 65% TCA group) and 30 of 32 patients (94%) (the 100% TCA group) experienced a good clinical response. All patients in the 100% TCA group who received five or six courses of treatment showed excellent results. Good satisfaction rates in the 65% and 100% TCA groups were recorded. There were no cases of significant complication. CONCLUSION: CROSS is a safe and very effective single modality for the treatment of atrophic acne scars with no significant complications.
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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 | 0.001 | 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) | 0.004 | 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".