Risk factors for revision of primary total hip replacement: Results from a national case–control study
Bibliographic record
Abstract
OBJECTIVE: To study the risk factors for revision of primary total hip replacement (THR) in a US population-based sample. METHODS: Using Medicare claims, we identified beneficiaries from 29 US states who underwent primary THR between July 1, 1995 and June 30, 1996, with followup through December 31, 2008. Potential cases had International Classification of Diseases, Ninth Revision, Clinical Modification codes indicating a revision THR. Each case was matched by state with 1 control THR recipient who was alive and unrevised when the case had a revision THR. We abstracted hospital records to document potential risk factors. We examined the associations between preoperative factors and revision risk using multivariate conditional logistic regression. RESULTS: The analysis data set contained 719 of 836 case-control pairs with complete data for analysis variables. The factors associated with higher revision odds in multivariate models were age ≤75 years at primary surgery (odds ratio [OR] 1.52 [95% confidence interval (95% CI) 1.20-1.92]), height in the highest tertile (OR 1.40 [95% CI 1.06-1.85]), weight in the highest tertile (OR 1.66 [95% CI 1.24-2.22]), cemented femoral component (OR 1.44 [95% CI 1.10-1.87]), prior contralateral primary THR (OR 1.36 [95% CI 1.05-1.76]), other prior orthopedic surgery (OR 1.45 [95% CI 1.13-1.84]), and living with others (versus alone; OR 1.26 [95% CI 0.99-1.61]). CONCLUSION: This first US population-based case-control study of risk factors for revision of primary THR showed that younger, taller, and heavier patients and those receiving a cemented femoral component had a greater likelihood of undergoing a revision THR over a 12-year followup period. Effects of age and body size on revision risk should be addressed by clinicians with patients considering primary THR.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".