Use of antidepressants following hysterectomy with or without oophorectomy: A national sample in the US
Bibliographic record
Abstract
OBJECTIVE: Hysterectomy is one of the most common gynecological surgeries conducted around the world. Previous studies reported inconsistent results on depressive symptoms experienced after hysterectomy. This study explored the association between hysterectomy with or without oophorectomy and the use of antidepressants. STUDY DESIGN: This cross-sectional study included 4888 subjects between 20 and 80 years old who participated in the US National Health and Nutrition Examination Survey (NHANES) between 2015 and 2018. The associations between hysterectomy with or without oophorectomy and the use of antidepressants were estimated using multivariable logistic regression models. MAIN OUTCOME MEASURES: There was a positive relationship between hysterectomy, both with and without oophorectomy, and the use of antidepressants after adjusting for all potential confounders (OR = 2.13, 95 % CI = 1.43-3.17, p = 0.000; OR = 2.04, 95 % CI = 1.35-3.06 p = 0.001). In the subgroup analysis stratified by race, a positive association between hysterectomy without oophorectomy was found among non-Hispanic white women (OR = 1.89, 95 % CI = 1.04-3.44, p = 0.038) and women of other races (OR = 3.14, 95 % CI = 1.30-7.56, p = 0.010), and a positive association between hysterectomy with oophorectomy was found among non-Hispanic black women (OR = 3.09, 95 % CI = 1.15-8.27, p = 0.024). However, no association was found among non-Hispanic black and Mexican American women who had undergone hysterectomy with oophorectomy, and it was not reported in women of non-Hispanic white, Mexican American or other race who underwent hysterectomy with oophorectomy. CONCLUSION: This study suggested that hysterectomy was significantly associated with antidepressant use, but the extent of the associations may vary by race.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".