Practical Aspects of Acne Scar Management: ASAP 2024
Bibliographic record
Abstract
Acne scars are one of the most common complications of acne. They can significantly affect the patient's quality of life. Often, several types of atrophic acne scars are observed simultaneously; therefore, consideration must be given to the type of scar while choosing the treatment modality. Effective treatment is not only important to prevent and improve acne scars but also crucial in preventing psychosocial effects. Treatment of acne scars requires an algorithmic approach that targets each component of the scars, and combination therapy on a patient-specific basis may offer the best chance for significant improvement. The goal of the current article is to discuss the practical aspects of management of atrophic acne scars using the vast modalities of treatment available. The panel of dermatologists and plastic surgeons, each one with at least 20 years of experience in acne scar treatment, participated in a series of 'Practical Aspects of Acne Scar Management' (ASAP) meetings: ASAP 2024. ASAP meetings were organized by "Scar Forum India" from March 2023 to July 2023 in four Indian cities (Mumbai, Delhi, Bengaluru, and Kolkata), each one for a duration of at least three hours. During these meetings and discussions, panelists reviewed and discussed the acne scar-related literature, their clinical experience in its management, available treatment options, along with recent advances. Consequently, a summary of the discussion and practical approach for the management of acne scars is developed. It was concluded that, though there is no specific guideline available to optimize acne scar management despite the multitude of treatment options, the best results can be achieved through the synergy of multiple treatment modalities and using the algorithmic approach.
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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 | 0.000 | 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 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".