Prevalence and Risk Factors of Acne Scarring Among Patients Consulting Dermatologists in the USA
Bibliographic record
Abstract
Although there have been few formal studies, scarring is a known bothersome companion of acne vulgaris. We performed a prospective study of subjects consulting a dermatologist for active acne to assess the frequency of acne scarring. Investigators performed a short questionnaire on all acne patients seen at their office for one consecutive 5-day work week to assess scar frequency. Additionally, the first four subjects with acne scars identified were enrolled for a second phase (scar cohort) of the study during which the investigator collected further medical history and performed a clinical evaluation and the patient completed a self-administered questionnaire about scar perceptions and impact on quality of life. A total of 1,972 subjects were evaluated by 120 investigators. Among these, 43 percent (n=843) had acne scarring. Subjects with acne scars were significantly more likely to have severe or very severe acne (P less than .01); however, 69% of the subjects with acne scars had mild or moderate acne at the time of the study visit. Risk factors correlated with increased likelihood of scarring were acne severity, time between acne onset and first effective treatment, relapsing acne, and male gender. Treatments that can completely resolve acne scars are not yet available - prevention and early treatment remain a primary strategy against scars. It is vital for clinicians who manage individuals with acne to institute effective therapy as early as possible, since treatment delay is a key modifiable risk factor for scarring.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".