A Consensus on Acne Management Focused on Specific Patient Features
Bibliographic record
Abstract
BACKGROUND: Most treatment guidelines for acne are based on clinical severity. Our objective was to expand that approach to one that also comprised individualized patient features: a case-based approach. METHODS: An expert panel of Canadian dermatologists was established to develop demographic and clinical features considered to be particularly important in acne treatment selection. A nominal group consensus process was used for inclusion of features and corresponding appropriate treatments. RESULTS: Consensus was achieved on the following statements: follicular epithelial dysfunction contributes to acne pathogenesis; inflammation from underlying disease(s) or prior treatment may impact further patient management; management focusing on specific patient features and on addressing psychosocial factors, including impact on quality of life, may improve treatment adherence and outcomes; and case-based scenarios are a practical approach to illustrate the effect of these factors. To address the latter, eight case profiles were developed. CONCLUSIONS: Management of acne should be based on multifactorial considerations beyond clinically determined acne severity and should include patient-reported impact, gender, skin sensitivity (including preexisting dermatoses), and phototype.
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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 | 0.056 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".