Risk of abnormal uterine bleeding associated with high-affinity compared with low-affinity serotonin and norepinephrine reuptake inhibitors
Bibliographic record
Abstract
BACKGROUND: Concerns have been raised about the potential association between selective serotonin reuptake inhibitors (SSRIs)/serotonin-norepinephrine reuptake inhibitors (SNRIs) and the risk of abnormal uterine bleeding (AUB), which may be influenced by the affinity of SSRIs/SNRIs for serotonin transporter. Thus, we assessed whether SSRIs/SNRIs with high-affinity for serotonin transporter are associated with AUB compared to SSRIs/SNRIs with low-affinity in non-pregnant women. METHODS: Using the UK Clinical Practice Research Datalink, we identified a cohort of women aged 15-24 years, newly prescribed a high- or low-affinity SSRI/SNRI between 1990 and 2019. Confounding was addressed using standardized morbidity ratio weighting. We used weighted Cox proportional hazards models to estimate the hazard ratio (HR) and 95 % confidence interval (CI) of AUB associated with high-affinity compared with low-affinity SSRIs/SNRIs. We assessed the risk of anemia as a secondary outcome. RESULTS: The cohort included 156,307 users of high-affinity SSRIs/SNRIs and 102,631 users of low-affinity SSRIs/SNRIs. High-affinity SSRIs/SNRIs were not associated with an increased risk of AUB compared with low-affinity SSRIs/SNRIs (incidence rates: 46.3 versus 42.4 per 1000 person-years, respectively; HR 1.01, 95 % CI 0.93-1.09). Duration of use, age, and comorbidities did not modify the risk. However, high-affinity SSRIs/SNRIs were associated with an increased risk of anemia (HR 1.29, 95 % CI 1.04-1.61) compared with low-affinity SSRIs/SNRIs. LIMITATIONS: Residual confounding may still be present. CONCLUSIONS: The risk of AUB did not differ between high- and low-affinity SSRIs/SNRIs. However, the potential risk of anemia suggests the need for monitoring and further investigation of the risk of AUB with these medications.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".