Psychometric assessment of alopecia areata patients before and after dermatological treatment
Bibliographic record
Abstract
SUMMARY: PSYCHOMETRIC ASSESSMENT OF ALOPECIA AREATA PATIENTS BEFORE AND AFTER DERMATOLOGICAL TREATMENT Object: Alopecia Areata (AA) is reported to account for 2% of dermatology clinical visits with incidence figures ranging up to 3.5 %. The role of stressful life events in the appearance of AA is uncertain. Besides reports associating anxiety and affective disorders with the onset of AA, there has also been studies which did not confirm such an association. There have been reports documenting the efficacy of antidepressants in AA. We wanted to see whether dermatological treatment and recovery in AA patients induces any change in psychometric variables. We wanted to test whether the well-being of patients after dermatological treatment has any association to psychic factors. Method: Eighteen patients were recruited in the study and they were assessed with Beck Depression and Hopelessness Scales, State-Trait Anxiety Inventory, Toronto Alexithymia Scale and Brief Symptom Inventory. Patients were given steroid therapy with 3 week intervals and they all responded favorably to treatment. Three months after initial assessment patients were reassessed with the same psychometric scales. Results: No statistically significant change was found. Conclussions: Despite the limitations of the study, this result may be interpreted as the independence of dermatological recovery from psychological factors. A placebo-controlled study is necessitated to further validate the results of this study.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".