Restoring prearthritic alignment improves joint perception in medial unicompartmental knee arthroplasty
Bibliographic record
Abstract
Purpose: This study aims to investigate whether restoring prearthritic alignment improves joint perception in medial unicompartmental knee arthroplasty (UKA). Methods: This retrospective cohort study analysed 244 patients who underwent nonrobotic-assisted medial fixed-bearing UKA between 2015 and 2018 with a minimum 2-year follow-up. Patients were categorised into prearthritic and nonprearthritic alignment groups based on the difference between their postoperative alignment and prearthritic hip-knee-ankle angle. Postoperative outcomes, including the Forgotten Joint Score (FJS-12), Oxford Knee Score (OKS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Knee Injury and Osteoarthritis Outcome Score (KOOS), Oxford Knee Score (OKS) and University of California Los Angeles (UCLA) activity score were compared between the groups. Additional analysis was performed in a subgroup of patients with constitutional varus alignment (CPAK types I, IV and VII). Multivariable logistic regression was used to identify predictors of achieving a forgotten joint. Results: = 0.017) scores compared to those with nonprearthritic alignment. In the constitutional varus subgroup, prearthritic alignment was associated with higher FJS-12, UCLA, OKS and KOOS ADL scores. The prearthritic alignment group also had a higher likelihood of achieving the 'forgotten joint' state. A 1-degree deviation from prearthritic alignment was associated with a 21% decrease in the probability of achieving a forgotten joint. Conclusion: Restoring prearthritic alignment in UKA is associated with improved postoperative joint perception and function, especially in patients with constitutional varus alignment. This personalised alignment approach may lead to better outcomes than the traditional goal of neutral alignment. Further research with a longer follow-up period is required to validate these findings and explore their impact on prosthesis survival. Levels of Evidence: Level II.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".