The Quality-of-Life Benefits and Risk of Isotretinoin (Accutane) in Acne Treatment: A Systematic Review
Bibliographic record
Abstract
Acne vulgaris is a prevalent dermatological condition, particularly affecting adolescents and young adults. While often treated as a cosmetic issue, acne can significantly impair a patient's mental health and overall quality of life (QoL). Isotretinoin, commonly known as Accutane, is widely regarded as the most effective treatment for severe and persistent acne. However, its use is controversial due to a range of potential side effects, including psychiatric symptoms, teratogenicity, and other systemic complications. To better understand the risk-benefit profile of isotretinoin in acne treatment, we conducted a systematic review, focusing on the QoL benefits and risks associated with its use. Studies were selected through a comprehensive screening process following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The quality of included studies was assessed using the Newcastle-Ottawa Scale (NOS) for nonrandomized studies, such as case-control and cohort designs. Of the studies reviewed, several demonstrated significant improvements in patients’ QoL due to reduced acne severity, while others highlighted the risks, particularly in terms of mental health side effects. Our findings reveal that while isotretinoin significantly enhances QoL in many patients, it also carries a substantial risk for adverse effects. This review provides valuable insights for healthcare professionals when considering isotretinoin as a treatment option, emphasizing the need for close monitoring of patients during and after treatment.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".