Risk profile of non-cicatricial alopecia in females
Bibliographic record
Abstract
Background: Non cicatricial alopecia (NCA) is a common dermatological problem in female produces greater psychological distress. Understanding the risk factors and associations is essential for comprehensive assessments and effective management of various forms of hair loss. This study has been conducted with the aim to identify the possible risk profile of different types of NCA in female patients. Methods: A descriptive type of observational study was conducted to find out the risk profile of NCA in females. About 355 females with NCA attending the outpatient department of dermatology and venereology department, Bangladesh medical university, Bangladesh during study period were the study population. Data was collected through face-to-face interviews and clinical examinations along with laboratory investigations on all patients. Results: Among 355 female patients of NCA, age, duration of alopecia and family history of alopecia were significantly higher in patients with FPHL in comparison to patchy and diffuse pattern hair loss patients. Patients with diffuse alopecia had statistically significant association (p≤0.001) with CTD and history of taking OCP, oral steroid and hydroxychloroquine significantly more from the patients with other two patterns of NCA and the blood hemoglobin level was significantly lower in patients with diffuse alopecia. Conclusions: Diffuse alopecia was the most common type NCA, which was associated with connective tissue diseases (CTD); history of taking OCP, oral steroid and hydroxychloroquine and lower blood hemoglobin level. Age, duration of alopecia and family history of alopecia were significantly higher in patients with female pattern hair loss (FPHL) of NCA.
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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 | 0.000 | 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) | 0.003 | 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".