Understanding the Association Between Mental Health and Hair Loss
Bibliographic record
Abstract
This literature review aims to analyze the association between mental health disorders and various types of hair loss, including telogen effluvium, androgenetic alopecia, alopecia areata, and compulsive disorders such as trichotillomania. A comprehensive review of recent studies was conducted to explore the bidirectional relationship between psychiatric conditions and hair loss, with emphasis on neurobiological mechanisms and psychosocial consequences. Findings show that psychiatric disorders can contribute to or exacerbate hair loss, while hair loss may lead to psychological symptoms such as anxiety, depression, and body dysmorphic disorder. Proposed mechanisms include immune dysfunction, neuroendocrine imbalance, microinflammation, brain-derived neurotrophic factor (BDNF) depletion, gut-brain-skin axis dysregulation, and medication-induced disruptions in hair cycling. Furthermore, individuals with somatic symptom disorder may report hair loss in the absence of clinical findings, complicating diagnosis and care. This review concludes that an interdisciplinary treatment model integrating dermatological and psychiatric support is essential for accurate diagnosis, effective treatment, and overall patient well-being.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".