Functional improvement of unicompartmental knee arthroplasty compared with total knee arthroplasty for subchondral insufficiency fracture of the knee
Bibliographic record
Abstract
Abstract Background Subchondral insufficiency fracture of the knee (SIFK) causes acute knee pain in adults and often requires surgical management. Unicompartmental knee arthroplasty (UKA) and total knee arthroplasty (TKA) are the two most common surgical treatments for SIFK. While both UKA and TKA have their advantages, there is no consensus for SIFK localized on the medial compartment. We hypothesized that patients with SIFK treated with UKA would show superior patient-reported outcomes compared to those who underwent TKA. Methods A total of 90 patients with SIFK were included in the TKA (n = 45) and UKA (n = 45) groups, respectively. SIFK lesions were measured on MR images. Hip knee ankle (HKA) angle was obtained preoperatively and at the final follow-up. Patient-reported outcomes in the form of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Hospital Special Surgery (HSS) scores, and Knee Society Scores (KSS) were assessed preoperatively, post-operative 6, 12 months, and at the final follow-up. Results The UKA group had better range of motion of the knee preoperatively and postoperatively than the TKA group (p<.01 and p<.01). At 6 months, WOMAC score was better in the UKA group than the TKA group (p<.01). Both groups had a significant improvement in WOMAC, HSS, and KSS scores at the final follow-up compared to preoperative scores. There were no surgical complications in the TKA group, but the UKA group had 3 revision cases due to a bearing failure. Conclusion Unicompartmental arthroplasty and total joint arthroplasty can produce successful outcomes in patients with SIFK with proper patient selection, regardless of the size of SIFK lesion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".