Slight undercorrection following total knee arthroplasty results in superior clinical outcomes in varus knees
Bibliographic record
Abstract
PURPOSE: Restoration of correct alignment is one of the main objectives of total knee arthroplasty (TKA). However, the influence of residual malalignment on clinical and functional outcomes is currently uncertain. This study was therefore undertaken to ascertain its influence in patients undergoing TKA for varus osteoarthritis of the knee. METHODS: A cohort of 132 consecutive patients (143 knees) with pre-operative varus alignment was evaluated with a mean follow-up period of 7.2 years. Based upon the post-operative alignment, patients were stratified into three groups: neutral, mild varus, and severe varus. These groups were compared with respect to clinical and functional outcomes. RESULTS: All patients had post-operative improvements in Knee Society Score (KSS). Knees that were left in mild varus scored significantly better for the KSS and the Western Ontario and McMaster Universities Arthritis Index, compared with knees that were corrected to neutral and knees that were left in severe varus exceeding 6°. No revisions occurred in any of the groups at midterm follow-up. CONCLUSION: The results of this study contradict the conventional assumption that correction to neutral mechanical alignment leads to the best outcome following TKA. Patients with pre-operative varus had better clinical and functional outcome scores if the alignment was left in mild varus, as compared with patients with an alignment correction to neutral. LEVEL OF EVIDENCE: Therapeutic study, Level III.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".