Development of an atrophic acne scar risk assessment tool
Bibliographic record
Abstract
BACKGROUND: Acne is a chronic dermatological disease predominantly afflicting young adults and is often associated with the development of scars. Acne scarring is usually avoidable when acne is managed early and effectively. However, acne patients often fail to seek early treatment. New and innovative tools to raise awareness are needed. OBJECTIVE: This study presents the development and assessment of a tool aiming to assess the risk of atrophic acne scars. METHODS: A systematic literature review of clinical risk factors for acne scars, a Delphi-like survey of dermatological experts in acne and secondary data analysis, were conducted to produce an evidence-based risk assessment tool. The tool was assessed both with a sample of young adults with and without scars and was assessed via a database cross-validation. RESULTS: A self-administered tool for risk assessment of developing atrophic acne scars in young adults was developed. It is a readily comprehensible and practical tool for population education and for use in medical practices. It comprises of four risk factors: worst ever severity of acne, duration of acne, family history of atrophic acne scars and lesion manipulation behaviours. It provides a dichotomous outcome: lower vs. higher risk of developing scars, thereby categorizing nearly two-thirds of the population correctly, with sensitivity of 82% and specificity of 43%. CONCLUSION: The present tool was developed as a response to current challenges in acne scar prevention. A potential benefit is to encourage those at risk to self-identify and to seek active intervention of their acne. In clinical practice, we expect this tool may help clinicians identify patients at risk of atrophic acne scarring and underscore their requirement for rapid and effective acne treatment.
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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 | 0.022 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".