New Patient-Oriented Tools for Assessing Atrophic Acne Scarring
Bibliographic record
Abstract
INTRODUCTION: Scarring on visible areas such as the face is associated with negative psychological impact. Many patients with acne have clinically relevant scarring for which they seek treatment, implying that there is an impact on their lives. Currently there are no validated tools to assess the burden of atrophic acne scarring from the patient's perspective or to assess treatment benefit. METHODS: Two patient-reported outcome measures, the self-assessment of clinical acne-related scars (SCARS) and the facial acne scar quality of life (FASQoL) tools, both specific to facial atrophic acne scarring, were developed according to Food and Drug Administration guidance methodology. Patient interviews were conducted first to elicit patient-important concepts about scarring, then to validate patients' understanding of wording in the tools. These tools focus on symptoms (SCARS) and psychological and social well-being (FASQoL) and were designed to be suitable for self-completion and to be rapidly completed (2-5 min) within a clinical research setting. RESULTS: Concept elicitation interviews were conducted with 30 subjects and cognitive interviews with 20 subjects. With acne scarring, important concepts for patients included size, surface area affected, counts, and depth. The SCARS and FASQoL tools were shown to address relevant concepts that were easily understood by patients. CONCLUSION: Two patient-reported measures, SCARS and FASQoL, have been developed to help clinicians assess the severity and impact of acne scars. Responsivity of these instruments to treatment will require further evaluation. FUNDING: Galderma R&D, Sophia Antipolis, France.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".