Bibliographic record
Abstract
BACKGROUND: Lichen sclerosus (LS) is an inflammatory disease of the skin and mucous membranes. Its aetiology is still unknown. OBJECTIVES: To determine risk factors for genital LS in men. METHODS: In a case-control study, 73 patients with LS, consecutively diagnosed at the City Dispensary for Skin and Venereal Diseases in Belgrade, were compared with 219 male patients visiting the same institution because of tinea cruris. Univariate and multivariate logistic regression analyses were used for analysis of data collected. RESULTS: According to multivariate logistic regression analysis, risk factors for male LS were as follows: a personal history of genital injury [odds ratio (OR) 28·1, 95% confidence interval (CI) 5·2-150·8], vitiligo (OR 23·1, 95% CI 2·2-240·2), alopecia areata (OR 8·8, 95% CI 1·1-68·5) and hypercholesterolaemia (OR 3·1, 95% CI 1·1-8·2), and a family history of alopecia areata (OR 24·3, 95% CI 2·1-280·7), diseases of the thyroid gland (OR 9·1, 95% CI 2·3-36·2) and other autoimmune diseases (OR 8·6, 95% CI 1·3-58·6). CONCLUSIONS: The results of the present study are in line with the hypothesis that trauma of the penis is a possible trigger of symptoms in genetically predisposed individuals and that personal and family histories of autoimmune disorders are risk factors for male LS.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".