One Acne™: A holistic management approach to improve overall skin quality and treatment outcomes in acne with or without sensitive skin
Bibliographic record
Abstract
Acne and sensitive skin can take a profound toll on patients' well-being, which can be exacerbated if the conditions are experienced together. This narrative review aims to identify appropriate treatments to facilitate a holistic management approach to acne (One Acne™), sensitive skin, and acne-induced sequelae and describe the role of treatments in improving skin quality. Topical retinoids are considered the preferred first-line option for acne treatment by dermatologists, either as monotherapy or in combination with other treatments, because of their ability to target various aspects of the disease. Tretinoin, trifarotene, adapalene, and tazarotene have all been assessed in clinical studies for managing acne-associated scarring, with varying success, with the latter three reported to improve skin quality. Moreover, some corrective procedures, e.g., injectable non-animal stabilized hyaluronic acid (NASHA) fillers, have proven effective for treating acne scarring. Both treatment types may complement each other to provide optimal treatment outcomes and patient satisfaction, as observed in several patients receiving concomitant treatment with NASHA fillers/topical trifarotene. Adjunctive use of cleansers, moisturizers, and photoprotection-containing ingredients such as vitamin B3, glycerin, or pro-vitamin B3 may also complement drug/corrective treatments to reduce skin irritation and risk of scarring, as well as improve skin hydration, tone, and overall appearance. This narrative review highlights that comprehensive skincare regimens should be used throughout acne patients' journeys to reduce treatment-related irritation, improve treatment outcomes, adherence, and satisfaction, and enhance overall skin quality. Patients with sensitive skin should choose tailored skincare products to maintain skin barrier integrity and restore skin function.
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How this classification was reachedexpand
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) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".