An update on diagnosis and treatment of female pattern hair loss
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Female pattern hair loss (FPHL) is the most common cause of hair loss in women. It has a greater psychosocial morbidity than that of male pattern hair loss. The clinical presentation of FPHL is distinctive with hair thinning usually confined to the crown region of the scalp. The frontal hair line is usually spared; however, it can be affected in some patients. Miniaturization of terminal scalp hair and shortening of the anagen growth phase of the hair cycle results in growth of thinner and shorter hair fibers. Diagnosis is usually made clinically. Recent advances in digital image analysis has increased the use of dermatoscopy in the diagnosis of FPHL and as a consequence, reduced the need for doing skin biopsies. Many medical and surgical treatments are currently available with various success rates. In this review article, we discuss the major recent advances in the diagnosis and management of FPHL.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it