Gaps and recommendations for clinical management of truncal acne from the Personalising Acne: Consensus of Experts panel
Bibliographic record
Abstract
BACKGROUND: Truncal acne is common and burdensome for patients; however, there is paucity of evidence and guidance for the management of truncal acne. Currently, clinical practice guidelines provide very little guidance on the assessment or management of truncal acne. OBJECTIVES: To identify unmet needs in truncal acne and make recommendations to address clinical and management gaps using an international consensus. METHODS: The Personalising Acne: Consensus of Experts panel consisted of 13 dermatologists, who used a modified Delphi approach to reach a consensus on statements related to clinically relevant aspects of truncal acne evaluation and management. A consensus was defined as ≥75% of the panelists voting "agree" or "strongly agree." The voting was electronic and blinded. RESULTS: The panel identified gaps and made recommendations related to truncal acne identification, assessment, and grading; the evaluation of the impact on patients; and treatment goals and factors to be considered for its management. LIMITATIONS: The recommendations are based on expert opinion, in the absence of high-quality evidence. CONCLUSIONS: We highlighted addressing not just facial acne but also truncal acne during patient consultations. The recommendations made herein may help facilitate the care of patients who present with truncal acne, with or without facial acne.
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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 | 0.170 | 0.261 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.017 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".