Preserving coronal knee alignment of the knee (CPAK) in unicompartmental knee arthroplasty correlates with superior patient-reported outcomes
Bibliographic record
Abstract
BACKGROUND: The optimal alignment target for unicompartmental knee arthroplasty (UKA) remains controversial, and literature suggests that its impact on patient-reported outcome measures (PROMs) varies. The purpose of this study was to identify the relationship between changes in the coronal plane alignment of the knee (CPAK) and PROMs in patients who underwent UKA. METHODS: A retrospective analysis of 164 patients who underwent UKA was conducted. The types of CPAK types categorized into unchanged, minor (shift to an adjacent CPAK type, e.g., type I to II or type I to IV), and major changes (transitioning to a nearby diagonal CPAK type or two types across, such as type I to V or type I to III). PROMs were assessed preoperatively and 1 year postoperatively using the Hospital for Special Surgery (HSS) scores, Knee Society (KS) scores, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and Forgotten Joint Scores (FJS). Comparison was performed between patients who experienced and who did not experience any changes in the CPAK. RESULTS: Patients with preserved native CPAK alignment demonstrated significantly superior 1 year postoperative outcomes, with higher HSS, KS knee, and WOMAC pain scores (p = 0.042, p = 0.009, and p = 0.048, respectively). Meanwhile, the degree of change in CPAK did not significantly influence the PROMs, and patients who experienced minor and major changes in the CPAK showed comparable outcomes. CONCLUSION: Preserving the native CPAK in UKA procedures is important for achieving favorable clinical outcomes at 1 year postoperative. The extent of change in the CPAK type exerted a limited impact on PROMs, thus emphasizing the importance of change in alignment itself.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".