The relation between total joint arthroplasty and risk for serious cardiovascular events in patients with moderate-severe osteoarthritis: propensity score matched landmark analysis
Bibliographic record
Abstract
OBJECTIVE: To examine whether total joint arthroplasty of the hip and knee reduces the risk for serious cardiovascular events in patients with moderate-severe osteoarthritis. DESIGN: Propensity score matched landmark analysis. SETTING: Ontario, Canada. PARTICIPANTS: 2200 adults with hip or knee osteoarthritis aged 55 or more at recruitment (1996-98) and followed prospectively until death or 2011. MAIN OUTCOME MEASURE: Rates of serious cardiovascular events for those who received a primary total joint arthroplasty compared with those did not within an exposure period of three years after baseline assessment. RESULTS: The propensity score matched cohort consisted of 153 matched pairs of participants with moderate-severe arthritis. Over a median follow-up period of seven years after the landmark date (start of the study), matched participants who underwent a total joint arthroplasty during the exposure period were significantly less likely than those who did not to experience a cardiovascular event (hazards ratio 0.56, 95% confidence interval 0.43 to 0.74, P<0.001). Within seven years of the exposure period the absolute risk reduction was 12.4% (95% confidence interval 1.7% to 23.1%) and number needed to treat was 8 (95% confidence interval 4 to 57 patients). CONCLUSIONS: Using a propensity matched landmark analysis in a population cohort with advanced hip or knee osteoarthritis, this study found a cardioprotective benefit of primary elective total joint arthroplasty.
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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 | 0.002 | 0.006 |
| 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 | 0.000 |
| 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".