Identifying gaps and providing recommendations to address shortcomings in the investigation of acne sequelae by the Personalising Acne: Consensus of Experts panel
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The physical sequelae of acne include erythema, hyperpigmentation, and scarring, which are highly burdensome for patients. Early, effective treatment can potentially limit and prevent sequelae development, but there is a need for guidance for and evidence of prevention-oriented management to improve patient outcomes. OBJECTIVE: To identify unmet needs of acne sequelae and generate expert recommendations to address gaps in clinical guidance. METHODS: The Personalizing Acne: Consensus of Experts panel of 13 dermatologists used a modified Delphi approach to achieve a consensus on the clinical aspects of acne sequelae. A consensus was defined as ≥75% of the dermatologists voting "agree" or "strongly agree." All voting was electronic and blinded. RESULTS: The panel identified gaps in current guidance and made recommendations related to acne sequelae. These included identification and classification of sequelae, pertinent points to consider for patient consultations, and management aimed at reducing the development of sequelae. LIMITATIONS: The recommendations are based on expert opinion and made in the absence of high-quality evidence. CONCLUSIONS: The identified gaps should help inform future research and guideline development for acne sequelae. The consensus-based recommendations should also support the process of consultations throughout the patient journey, helping to reduce the development and burden of acne sequelae through improved risk factor recognition, early discussion, and appropriate management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it