Bibliographic record
Abstract
BACKGROUND: Gender differences in depression have been documented for many years and thought to be insignificant to treatment selection until recently. METHODS: This article reviews gender differences in the prevalence, presentation, etiology, and antidepressant treatment of depressive disorders. RESULTS: The high female to male sex ratio in the prevalence of depression, especially during the reproductive years, is one of the most replicated findings in epidemiology. Women more often have a seasonal component, anxious and atypical depression. Explanations for the differences include psychological, neurochemical, anatomic, hormonal, genetic, and personality factors. Gender differences in antidepressant treatment response have not been found consistently. Hormonal status may be an important variable in addition to the effects of the menstrual cycle, pregnancy, perimenopause and menopause. CONCLUSIONS: Women have higher rates of depression and can often present differently than do men. Further research can ascertain which combination of factors increase women's risk. The effect of pregnancy and the impact of the menstrual cycle on the course of all depressive disorders need increased attention. Large prospective randomized controlled trials with gender differences in treatment response as the primary endpoint are necessary in order to answer the now controversial question of gender differences in antidepressant treatment response.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".