Sex Differences in Complications Following Total Hip Arthroplasty: A Population-Based Study
Bibliographic record
Abstract
BACKGROUND: The relationship between sex and outcomes, especially complications, after total hip arthroplasty (THA) has not been well established. This study aimed to identify if patient biological sex significantly impacted complications after THA in Ontario, Canada. METHODS: A population-based retrospective cohort study of patients undergoing primary THA in Ontario from April 1, 2015 to March 31, 2020 was conducted. The primary outcome was major surgical complications within a year postsurgery (a composite of revision, deep infection requiring surgery, and dislocation). Secondary outcomes included the individual component of the composite primary outcome and major medical complications within 30 days. Proportional hazards regression calculated the adjusted hazards ratio for major surgical complications in men relative to women, adjusting for age, comorbidities, neighborhood income quintile, surgeon and hospital volume, and year of surgery. RESULTS: A total of 67,077 patients (median age 68 years; 54.1% women) from 61 hospitals were included; women were older with a higher prevalence of frailty. Women had a higher rate of major surgical complications within 1 year of surgery compared to men (2.9 versus 2.5%, adjusted odds ratio 1.19, 95% confidence interval 1.08 to 1.33, P = .0009). Conversely, men had a higher risk for medical complications within 30 days (6.3 versus 2.7%, P < .001). CONCLUSIONS: Observable sex disparities exist in post-THA complications; women face surgical complications predominantly, while medical complications are more prevalent in men. These insights can shape preoperative patient consultations. LEVEL OF EVIDENCE: Level III.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".