Reoperations After Operatively and Non-Operatively Treated Periprosthetic Knee Fractures: A Nationwide Study on 1,931 Fractures After Primary Total Knee Arthroplasty
Bibliographic record
Abstract
BACKGROUND: Periprosthetic knee fracture (PPKF) following total knee arthroplasty (TKA) can be difficult to treat. A PPKF can be treated both operatively and nonoperatively, and the treatment varies between fracture sites. This study aimed to assess the risk of reoperation according to the fracture site and treatment of the PPKF. METHOD: This study is a nationwide cohort study using register data from 1997 to 2022. Cruciate-retaining or posterior-stabilized primary TKA were identified from the Danish Knee Arthroplasty Register. Subsequent PPKFs, fracture treatments, and later reoperations were identified through the International Classification of Diseases, tenth edition (ICD-10) diagnosis and procedure codes in the Danish National Patient Register. In addition, indications for revision total knee arthroplasty (rTKA) in the Danish Knee Arthroplasty Register were used to identify PPKFs and the reason for reoperation. RESULTS: We included 1,931 PPKFs (1,494 femoral, 207 patellae, and 230 tibial) with an overall two-year reoperation risk of 20% (95% confidence interval (CI): 18 to 23). Femoral PPKFs had a two-year reoperation risk of 16% (CI: 12 to 22) after nonoperative treatment and 21% (CI: 18 to 24) after operative treatment. Patellar PPKFs had a two-year reoperation risk in five to 17 after nonoperative treatment and 46% (CI: 30 to 69) after operative treatment. Tibial PPKFs had a two-year reoperation risk of 17% (CI: 11 to 27) after nonoperative treatment and 36% (25 to 53) after operative treatment. CONCLUSIONS: The overall two-year reoperation risk was 20% (CI: 18 to 23). Operative-treated PPKFs had a higher risk of reoperation across all fracture locations. The highest reoperation risk was found after operatively treated patella and tibial PPKFs (46 and 36%, respectively), and the lowest reoperation risk was found after nonoperative-treated patella PPKFs.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".