The Question of Adolescent and Postadolescent Acne: The Nigerian Experience
Bibliographic record
Abstract
Introduction: Differences between adolescent and postadolescent acne are increasingly being recognized. This study aimed to document the clinical profile of facial acne vulgaris and, additionally, to compare adolescent to postadolescent acne and any gender-based differences. Methods: Cross-sectional descriptive study of 261 facial acne vulgaris patients was conducted from February 2021 to March 2022 at three dermatology clinics. Patients had their anthropometric measurements, type of acne lesions, and severity and scarring assessed. Results: A total of 261 patients (75.5% females) with a mean age of 24.5 (±7.4) years were diagnosed to have facial acne vulgaris. The severity of acne was mild in 44.8%, moderate in 48.3%, and severe in 6.9%. Acne was noninflammatory in 69.7%, inflammatory in 13.0%, and mixed in 17.2%. Adolescent and postadolescent acne significantly differed in the type of acne, BMI, type of acne lesions, and acne scarring. Gender-based differences included BMI, lesions of acne, and severity. Conclusion: There is an increasing prevalence of postadolescent acne with persistent being the most common category. There are significant differences between adolescent and postadolescent acne: type of acne, BMI, type of acne lesions, and acne scarring. Gender-based differences exist in both adolescent and postadolescent acne.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".