Bibliographic record
Abstract
Our understanding of the pathogenesis of acne vulgaris is still evolving.It is known that multiple factors impact acne pathophysiology, including genetic, hormonal, inflammatory, and environmental influences.Because of its implications in many of these factors, diet has been a part of the acne discussion for decades.Several studies have evaluated the significance of the glycemic index of various foods and glycemic load in patients with acne, demonstrating individuals with acne who consume diets with a low glycemic load have reduced acne lesions compared with individuals on high glycemic load diets.Dairy has also been a focus of study regarding dietary influences on acne; whey proteins responsible for the insulinotropic effects of milk may contribute more to acne development than the actual fat or dairy content.Other studies have examined the effects of omega-3 fatty acid and γ-linoleic acid consumption in individuals with acne, showing individuals with acne benefit from diets consisting of fish and healthy oils, thereby increasing omega-3 and omega-6 fatty acid intake.Recent research into the effects of probiotic administration in individuals with acne present promising results; further study of the effects of probiotics on acne is needed to support the findings of these early studies.In this review, we discuss the current evidence regarding the diets of US patients with acne and how they may impact acne and acne treatment. Key PointsDespite limitations in studies investigating diet and acne, there is evidence that glycemic index, dairy content, dietary fats, and probiotics may play a role in acne and its treatment.For all therapies prescribed, healthcare providers should provide patients with information on how their choice of diet could enhance their therapeutic outcomes and possibly reduce the risk of relapse upon treatment cessation.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".