Unicompartmental Versus Total Knee Arthroplasty Database Analysis: Is There a Winner?
Bibliographic record
Abstract
BACKGROUND: TKA and unicompartmental knee arthroplasty (UKA) are both utilized to treat unicompartmental knee arthrosis. While some surgeons assume UKA provides better function than TKA, this assumption is based on greater final outcome scores rather than on change in scores and many patients with UKA have higher preoperative scores. QUESTIONS/PURPOSES: We therefore asked whether TKA would demonstrate (1) better change in clinical outcome scores from preoperative to postoperative states and (2) better survivorship than UKA. METHODS: We evaluated 4087 patients with 5606 TKAs and 179 patients with 279 UKAs performed between 1978 and 2009. Patients with TKA were older and heavier than patients with UKA (mean age, 68 versus 66 years; mean BMI, 32 versus 29). We compared preoperative, latest postoperative, and change in Knee Society Clinical Rating System (KSCRS), SF-12, and WOMAC scores. Minimum followup was 2 years (UKA: mean, 7 years; range, 2.0-23 years; TKA: mean, 6.5 years; range, 2.0-33 years). Preoperative outcome measure scores (WOMAC, SF-12, KSCRS) were higher in the UKA group. RESULTS: Patients with UKA had higher postoperative KSCRS and SF-12 mental scores. Changes in score for all WOMAC domains were similar between groups. Total KSCRS changes in score were similar between groups, although patients with TKA had higher knee scores (49 versus 43) but lower function scores than UKA (21 versus 26). Cumulative revision rate was higher for UKA than for TKA (13% versus 7%). Kaplan-Meier survivorship at 5 and 10 years was 95% and 90%, respectively, for UKA and 98% and 95%, respectively, for TKA. CONCLUSIONS: While patients with UKA had higher pre- and postoperative scores than patients with TKA, the changes in scores were similar in both groups and survival appeared higher in patients with TKA.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".