Risk of hair loss with different antidepressants
Bibliographic record
Abstract
The aim of this study was to quantify the risk of hair loss with different antidepressants. A retrospective cohort study design using a large health claims database in the USA from 2006 to 2014 was utilized. A cohort of new user and mutually exclusive users of fluoxetine, fluvoxamine, sertraline, citalopram, escitalopram, paroxetine, duloxetine, venlafaxine, desvenlafaxine, and bupropion were followed to the first diagnosis of alopecia. The cohort was comprised of 1 025 140 new users of fluoxetine, fluvoxamine, sertraline, citalopram, escitalopram, paroxetine, duloxetine, venlafaxine, desvenlafaxine, and bupropion, with sertraline the most commonly prescribed (N=190 227) and fluvoxamine (N=3010) the least prescribed. Compared with bupropion, all other antidepressants had a lower risk of hair loss, with fluoxetine and paroxetine having the lowest risk [hazard ratio (HR)=0.68, 95% confidence interval (CI): 0.63-0.74, HR=0.68, 95% CI: 0.62-0.74, respectively] and fluvoxamine having the highest risk (HR=0.93, 95% CI: 0.64-1.37). Compared with fluoxetine, bupropion had the highest risk of hair loss (HR=1.46, 95% CI: 1.35-1.58, number needed to harm=242 for 2 years) and paroxetine had the lowest risk (HR=0.99, 95% CI: 0.90-1.09). The results of this large population-based cohort study suggest an increase in the risk of hair loss with bupropion compared with selective serotonin reuptake inhibitors and selective norepinephrine reuptake inhibitors, whereas paroxetine had the lowest risk.
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 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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".