The effect of leg length discrepancy on clinical outcome after TKA and identification of possible risk factors
Bibliographic record
Abstract
PURPOSE: This study was investigated on the leg length discrepancy (LLD) after computer-assisted total knee arthroplasty (TKA), and its effects on the post-operative function and patient satisfaction. It is hypothesized that LLD after computer-assisted TKA would affect the clinical outcomes for knee scores. METHODS: A total of 148 cases were analysed retrospectively with a minimum of 2 years of follow-up. Eighty-one knees involved a <15-mm LLD, and 67 knees involved more than a 15-mm LLD. The radiographic outcomes, clinical outcomes, patients' satisfaction, and perception of LLD were also evaluated. RESULTS: There was a significant difference in the Knee Society function score and the score for the difficulty with ascending the stairs in the Western Ontario and McMaster Universities score between the groups. There was a statistically significant difference between the two groups in the results of their perception questionnaires, but no difference in the results of their satisfaction questionnaires. The odds ratio for the risk of post-operative LLD increased with the increased pre-operative LLD and the unilateral TKA. CONCLUSIONS: In conclusion, the functional outcomes of more than 15-mm post-operative LLD after computer-assisted TKA were lower than those of the <15-mm LLD. Thus, the reduced post-operative LLD should be considered to improve the functional outcomes of primary TKA. A careful treatment plan for degenerative arthritis should be considered and discussed with patients, especially in unilateral TKAs. LEVEL OF EVIDENCE: IV.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".