ANDROGEN-DEPENDENT DERMOPATHY IN WOMEN WITH KELOID SCARS
Bibliographic record
Abstract
Objective: To explore the character of androgen-dependent dermopathy (ADD) in women with keloid scars. Methods: 100 girls and women aged 15-28 years were examined, of whom 47 were with «true» and 53 were with «false» keloids. The evaluation of keloid scars was carried out using the Vancouver Scale (Baryza MJ, Baryza GA, 1995), modified Fistal HH (2006). Hyperandrogenic skin conditions were evaluated using the dermatological acne index (DAI), the Ferriman-Galway scale and the trichoscopy method. Results: Acne was detected in 47 (100%) patients with «true» keloids and 40 (75.5%) patients with «false» keloids. Seborrheic dermatitis occurred in 32 (68.1%) cases of «true» and 27 (51.0%) observations of «false» keloids. In both groups of patients, the borderline condition between normal and excess haired, which in 22 (46.8%) patients with «true» keloids amounted to 10,5±1,2. Signs of androgenic alopecia were detected in 35 (74.5%) patients with «true» and 38 (71.7%) patients with «false» keloids. In «true» keloids, the density of hair in the androgen-dependent (parietal) area was lower than in patients with «false» keloid scars (171.3±14.6 vs. 273.2±17.5). The androgen-independent occipital area, the density of hair in patients with «true» scars was lower (191.3±11.2), than in patients with «false» keloids (241.0±18.5). In all patients with «true» keloids observed a combination of scarring with ADD. In 13 (24.5%) patients with «false» keloids correlation with ADD was not observed. Conclusion: The most typical ADD in women with keloid scars are the acne and androgenic alopecia, which occur respectively, in 100% and 74.5% of cases in «true», and in 75.5% and 71.6% with «false» keloids. Consequently, hyperandrogenic skin conditions in women may be risk factors for keloids and indicate a hormonal imbalance in this contingent of patients. Keywords: Androgen-dependent dermopathy, keloids , acne, androgenic alopecia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".