Competing Risks Methods Are Recommended for Estimating the Cumulative Incidence of Revision Arthroplasty for Health Care Planning Purposes
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Bibliographic record
Abstract
Cumulative incidence of revision provides a measure of the failure rate of joint replacements and can be used to project demand for revisions. The most commonly applied survival analysis method (Kaplan–Meier [KM]) does not account for competing risks (eg, death). The authors compared the cumulative incidence function (CIF), a competing risks method, with the KM method through application to population-based cohorts. They measured time to revision, death, or censoring for unilateral total hip arthroplasty (THA; n=12,496) and total knee arthroplasty (TKA; n=19,172) cohorts in administrative databases in Alberta and TKAs (n=80,177) in the Swedish Knee Arthroplasty Register. The authors compared relative differences between the KM and CIF. They fitted Cox, Fine and Gray, and Royston and Parmar regression models and compared coefficients, standard errors, and P values. On sensitivity analysis, the authors included staged bilateral operations. Kaplan–Meier estimates exceeded the CIF at each time point. The magnitude of overestimation increased with follow-up time and was greatest for the Swedish cohort. At 5 years, relative differences between KM and CIF estimates for the Alberta THA and TKA and Swedish TKA cohorts were 1.8%, 2.3%, and 3.8%, respectively. These differences increased to 3.1%, 5.8%, and 8.2%, respectively, at 9 years, reaching 39.1% at 20 years (Swedish cohort). On sensitivity analysis (including staged bilateral operations), the Fine and Gray subdistribution hazard ratio differed from the Cox and Royston and Parmar hazard ratios. When the frequency of competing risks is high, competing risks methods are recommended to obtain accurate cumulative incidence estimates for informing health care planning and decision making. [ Orthopedics . 2021;44(4):e549–e555.]
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it