Prevalence and severity of facial and truncal acne in a referral cohort.
Bibliographic record
Abstract
BACKGROUND: There is a paucity of information on the prevalence and severity of acne of the face, chest, and back. PURPOSE: This study was designed to examine the prevalence and severity of acne on the face, chest, and back in a referral cohort of patients with acne using a validated global acne severity scale. METHODS: Acne patients referred to dermatologists were evaluated at the face, chest, and back. Chi-square testing was performed to assess consistency between patient and physician assessments of each region. The correlation of acne severity between regions was evaluated by Spearman's rank correlation. RESULTS: In 965 patients, the prevalence of acne on the face, chest, and back was 92%, 45%, and 61%, respectively. Acne severity was significantly correlated for all regional pairs (P<.001): face and back (r=0.11); face and chest (r=0.12); and chest and back (r=0.67). The consistency of patient reporting and clinical evaluation for the presence of acne varied by region: face=92%, chest=69%, and back=74%. The proportions of patients reporting no occurrence of acne when clinical acne was indeed absent (negative predictive value) were 67% and 65% for the chest and back, respectively. LIMITATIONS: The operational threshold for clinical acne (>mild) may underestimate the total proportion of affected patients. These patients were referred to dermatologists for care and may represent a more severe cohort. CONCLUSION: Acne affected the face in 92% and the trunk in just over 60% (with the back more frequently and severely affected than the chest). Acne severity was observed to have a much higher correlation between chest and back than face and back or face and chest. Patient-reporting evaluations of absence of acne on the chest and back are frequently erroneous, mandating clinical evaluations of these sites for assessment of overall extent.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".