Functional Outcome of Subvastus versus Medial Parapatellar Approaches for Total Knee Replacement in Patients with Knee Osteoarthritis: A Prospective Cohort Study
Bibliographic record
Abstract
Abstract Background: Subvastus approach and medial parapatellar approach are two major approaches for total knee replacement (TKR). There is no global consensus on the superiority of either approach in terms of functional outcomes. Objective: The present study aimed to evaluate the functional outcome of TKR through subvastus approach and medial parapatellar approach by using patient-reported scores at 3-, 6-, and 12-month post-operative follow-ups. Methods: This prospective cohort follow-up study included patients with knee osteoarthritis who underwent elective primary TKR either through the subvastus or medial parapatellar approaches at King Abdullah Medical City, Makkah city, Kingdom of Saudi Arabia, from January 2019 to December 2022. Scores from the self-reported Oxford Knee Score (OKS) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were compared in the two groups of patients at 3-, 6-, and 12-month post-operative follow-ups. Results: A total of 98 patients were included, of which 37 underwent TKR through the subvastus approach and 61 through the medial parapatellar approach. There was an overall significant change over time in both WOMAC and OKS scores ( P < 0.001). Patients who underwent the subvastus approach had significantly higher mean of WOMAC and OKS than patients with the medial parapatellar approach at the 3- and 6-month follow-ups ( P < 0.05), but not at the 12-month follow-up. Conclusions: For TKR, the medial parapatellar approach results in better functional outcomes at the 3- and 6- month follow-up periods compared with the subvastus approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".