Pilot study for the evaluation and adaptation of a Four Item-Acne-Scar Risk Assessment Tool (4-ASRAT): a resource to estimate the risk of acne-induced scars
Bibliographic record
Abstract
Background: Acne-induced scarring is associated with a similar burden as acne, i.e. diminished quality of life, and may be avoided if patients receive appropriate and timely acne treatment. In 2017, a four item-Acne-Scar Risk Assessment Tool (4-ASRAT) was designed by Tan et al . to categorise patients with acne into lower-risk or higher-risk for acne scarring. Its applicability outside the initial study population (France, Brazil and United States) remains to be determined. Methods: A study protocol was developed to create a systematic approach for validating and adapting 4-ASRAT to different populations, Ecuador in this case. The protocol was reviewed by 11 local and international dermatologists and pilot-tested in an Ecuadorian population using a sample of 10 participants who currently had or had had acne. Feedback from the pilot study was used to improve the study protocol. The results of the pilot study are included here, and the final study protocol is available as extended data. Results: The protocol proved to be applicable. Images taken of participants were a valuable resource for dermatological evaluation about the presence or absence of acne scars. Tangential light is necessary for this evaluation. Although dermatological assessments varied, we concluded that assessment by three local dermatologists for each participant was adequate for reaching a consensus on the presence or absence of acne scars. Conclusions: Considering the morbidity related to acne and acne scars, tools designed as prevention that alert patients about risk of developing scarring are necessary. The proposed protocol shows a feasible way of validating and adapting 4-ASRAT to different populations.
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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 | 0.059 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".