Associations between selective serotonin reuptake inhibitors and adverse events following hip fracture arthroplasty: a retrospective cohort study
Bibliographic record
Abstract
Background: Hip fractures are a priority topic while selective serotonin reuptake inhibitors (SSRI) use is increasing. Surgical outcomes over longer follow-up periods for hip fracture patients on SSRIs is unclear. The purpose of this study was to test for associations between SSRIs and post-surgical adverse events for hip fracture arthroplasty patients. Methods: Hospital data were used to select patients who had hip fracture arthroplasty surgery in Nova Scotia, Canada from 2016 to 2022. Patients who filled an SSRI prescription (Rx) in the 180-day period prior to surgery were identified. Study outcomes were any emergency department (ED) visit, mortality, revision, and major bleeding within 180 days of discharge as well as a blood transfusion during admission. Multivariate hierarchical logistic models weighted by inverse probability treatment weights were estimated to test for associations between SSRI use and outcomes. Results: An SSRI prescription was filled in the 180-day pre-surgery period for (883) 29.9% of the 2946 cases. Adjusted odds ratios were higher for those on an SSRI for an ED visit (1.68 CI, 1.40–2.01; p < 0.0001), mortality (1.26 CI, 1.02–1.55; p = 0.036), revision (2.35 CI, 1.36–4.06; p = 0.0022), and bleeding event (1.48 CI 1.06–2.07; p = 0.022). Blood transfusion was statistically insignificant. Discussion: SSRI use was associated with worse outcomes for hip fracture patients for four of five study outcomes. SSRI use should be discussed prior to surgery to mitigate the likelihood of adverse events.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".