Prevalence and Demographics of Truncal Involvement Among Acne Patients: Survey Data and a Review of the Literature.
Bibliographic record
Abstract
Background: Truncal acne is frequently underdiagnosed despite affecting around half of those with facial acne. The objective was to provide an overview of the literature on the incidence of truncal acne according to age, gender, and acne severity. Methods: A narrative review of data from recent large surveys and a literature search in PubMed on the incidence of truncal acne across subgroups of age, gender, and acne severity. Results: The prevalence of truncal acne alone was low, ranging from <1% to 14%, but approximately 30 to 60 percent of individuals with facial acne also had truncal acne depending on the population. In an online survey in the United States of 2,000 respondents aged between 14 -29 years with self-reported active facial and/or truncal acne, the incidence of truncal acne was lower in the 14-20 years subgroup than in the 21-29 years subgroup (49% vs 54%). The incidence of truncal acne was similar in both males and females, while 46 percent of respondents with self-declared clear and mild acne indicated having truncal involvement compared to 60 percent of those with moderate or severe acne. Limitations: Online surveys have inherent limitations, such as self-reporting and potential confounders. Conclusion: Data suggests that patients with both facial and truncal involvement have earlier onset of acne and more severe acne. Additional adverse psychological impact may arise from having the impression that the disease is spreading and becoming more severe. Raising awareness of truncal acne prevalence and demographics could improve its clinical management to reduce the negative psychological impact.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".