Effect of Timing and Duration of Statin Exposure on Risk of Hip or Knee Revision Arthroplasty: A Population-based Cohort Study
Bibliographic record
Abstract
OBJECTIVES: To determine whether the timing and duration of statin exposure following total hip/knee arthroplasty (THA/TKA) influence the risk of revision arthroplasty. METHODS: Subjects from the Clinical Practice Research Datalink, a large population-based clinical database, who had THA/TKA from 1988 to 2016, were included. Propensity score adjusted Cox regression models were used to determine the association between statin exposure and the risk of revision THA/TKA, (1) at any time, and (2) if first exposed 0-1, 1-5, or > 5 years following THA/TKA. We also investigated the effect of duration of statin exposure (< 1, 1-2, 2-3, 3-4, 4-5, > 5 yrs). RESULTS: The study included 151,305 participants. There were 65,032 (43%) exposed to statins during followup and 3500 (2.3%) had revision arthroplasty. In a propensity score adjusted model, exposure to statins was associated with a reduced risk of revision arthroplasty (HR 0.82, 95% CI 0.75-0.90). Participants first exposed within 1 year and between 1 and 5 years following THA/TKA (vs unexposed) had a reduced risk of revision arthroplasty (HR 0.82, 95% CI 0.74-0.91 and HR 0.76, 95% CI 0.65-0.90, respectively). In relation to duration of statin therapy, participants exposed for more than 5 years in total (vs < 1 yr) had a reduced risk of revision (HR 0.74, 95% CI 0.62-0.88). CONCLUSION: Statin therapy initiated up to 5 years following THA/TKA may reduce the risk of revision 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".