Impact of facial and truncal acne on quality of life: A multi-country population-based survey
Bibliographic record
Abstract
Background Acne confers an increased risk of physical, psychiatric, and psychosocial sequelae, potentially affecting multiple dimensions of health-related quality of life (HRQoL). Morbidity associated with truncal acne is poorly understood. Objective To determine how severity and location of acne lesions impact the HRQoL of those who suffer from it. Methods A total of 694 subjects with combined facial and truncal acne (F+T) and 615 with facial acne only (F) participated in an online, international survey. Participants self-graded the severity of their acne at different anatomical locations and completed the dermatology life quality index (DLQI). Results The F+T participants were twice as likely to report "very large" to "extremely large" impact on HRQoL (ie, DLQI > 10 and children's DLQI [CDLQI] > 12) as compared with the F participants (DLQI: odds ratio [OR] 1.61 [95% confidence interval {CI} 1.02-2.54]; CDLQI: OR 1.86 [95% CI 1.10-3.14]). The impact of acne on HRQoL increased with increasing acne severity on the face (DLQI and CDLQI P values = .001 and .017, respectively), chest ( P = .003; P = .008), and back ( P = .001; P = .028). Limitations Temporal evaluation of acne impact was not estimated. Conclusions Facial and truncal acne was associated with a greater impact on HRQoL than facial acne alone. Increasing severity of truncal acne increases the adverse impact on HRQoL irrespective of the severity of facial acne.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".