Incidence and timing of postoperative complications after total hip and knee arthroplasty
Bibliographic record
Abstract
BACKGROUND: Follow-up protocols after total hip or knee arthroplasty (THA or TKA, respectively) have little uniformity, which can lead to emergency department (ED) visits for postoperative complications. We sought to determine the incidence and timing of postoperative complications after THA or TKA. METHODS: We conducted a population-based retrospective cohort study of all adults in Ontario who underwent primary THA or TKA between 2010 and 2019. We used data available through ICES. We identified medical and surgical complications, ED visits, and hospital readmissions using institutional databases and Ontario Health Insurance Plan claims. Outcomes included major medical complications within 30 days and surgical complications within 1 year after surgery. RESULTS: We included 158 503 and 103 728 patients who underwent TKA and THA, respectively. The incidence of medical complications within 30 days was 2.90% after TKA and 2.42% after THA. Visits to the ED (20.0% after TKA, 16.9% after THA) and readmission rates within 30 days (3.8% after TKA, 4.1% after THA) were similar for both groups. Visits to the ED occurred at a median of 10 days after surgery for both groups, with readmissions at a median of 12 and 13 days after TKA and THA, respectively. The incidence of major TKA complications was 1.6%, with a median time of 84 (interquartile range [IQR] 26-224) days. The incidence of major THA complications was 2.2%, with a median time of 29 (IQR 16-80) days. CONCLUSION: Our findings suggest follow-up contact 7-10 days after THA or TKA to minimize ED visits, with at least 1 subsequent in-person follow-up at 5-6 weeks after surgery. After that, surgeons may personalize additional follow-ups as needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".