Serotonergic antidepressants and increased bleeding risk in patients undergoing breast biopsy
Bibliographic record
Abstract
OBJECTIVES: Recent investigations have shown that serotonergic antidepressant (SAd) use may increase the risk of peri-operative bleeding events. Our objective was to evaluate the possibility of a similar association in patients undergoing radiologic breast biopsies. METHODS: We used data from 3890 patients undergoing 6300 biopsy procedures between January 2011 and October 2014 in the Breast Clinic of McGill University Health Centre, Montreal, Canada. In this case-control study, cases were patients reported to have abnormal bleeding during their biopsy by board-certified radiologists. A control group of nonbleeders was selected using matching according to age and type of biopsy. The correlation between abnormal bleeding and SAd use was assessed using bivariate and multivariate statistical analyses. RESULTS: There were 97 patients with abnormal bleeding and 137 matched controls; 10 bleeders (cases) were on SAds (7 citalopram, 3 paroxetine) while only 1 nonbleeder (control group) was on a SAd (low-dose sertraline, 25 mg/day). SAds were significantly associated with increased bleeding risk (10.3% versus 0.7%, Fisher's Exact p = 0.001). Moreover, after adjusting for confounding factors (age, type of biopsy, size of biopsy, needle caliber, pathology result and nonsteroidal anti-inflammatory drug use, multivariate logistic regression confirmed that SAds were associated with elevated bleeding risk (16.2, 95% confidence interval 1.87-140.1, p = 0.01). CONCLUSIONS: This is the first study demonstrating increased bleeding events in breast biopsy patients using SAds. Clinicians should be aware that SAds may be associated with peri-operative bleeding risk, even in relatively minor procedures such as breast biopsies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 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".