The Personalized Acne Treatment Tool — Recommendations to facilitate a patient-centered approach to acne management from the Personalizing Acne: Consensus of Experts
Bibliographic record
Abstract
Background: Acne, a commonly treated skin disease, requires patient-centered management due to its varying presentations, chronicity, and impact on health-related quality of life. Despite this, evidence-based clinical guidelines focus primarily on clinical severity of facial acne, omitting important patient- and disease-related factors, including ongoing management. Objectives: To generate recommendations to support patient-centered acne management, which incorporate priority and prognostic factors beyond conventional clinical severity, traditionally defined by grading the appearance and extent of visible lesions. Methods: The Personalizing Acne: Consensus of Experts consisted of 17 dermatologists who used a modified Delphi approach to reach consensus on statements regarding patient- and treatment-related factors pertaining to patient-centered acne management. Consensus was defined as ≥75% voting "agree" or "strongly agree." Results: Recommendations based on factors such as acne sequelae, location of acne, high burden of disease, and individual patient features were generated and incorporated into the Personalized Acne Treatment Tool. Limitations: Recommendations are based on expert opinion, which may differ from patients' perspectives. Regional variations in healthcare systems may not be represented. Conclusions: The Personalizing Acne: Consensus of Experts panel provided practical recommendations to facilitate individualized management of acne, based on patient features, which can be implemented to improve treatment outcomes, adherence, and patient satisfaction.
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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 | 0.095 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".