Does surgical approach affect patient outcomes of total knee arthroplasty?
Bibliographic record
Abstract
Background: Surgical approaches for total knee arthroplasty (TKA) include the medial parapatellar (MPA), subvastus (SV), midvastus (MV), and lateral parapatellar approach (LPA); it remains unclear which approach is superior. Methods: Patients having undergone TKA at our institution were retrospectively organized into matched groups according to surgical approach (MPA, MV, SV, or LPA). Outcomes between the groups were compared using the Short-Form 12 (SF-12), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Knee Society Score (KSS), and range of motion (ROM) up to 2 years postoperative. Results: Sixty-eight MV patients, 8 SV patients, and 4 LPA patients were matched with groups of MPA patients. There was no difference in outcomes between the MPA and MV groups up to 2 years. The SV group had significantly higher SF-12 Physical Composite Score (PCS; p = 0.036) and WOMAC stiffness score (p = 0.014) at 2 years, but significantly lower flexion at 1 year (p = 0.022) than the MPA group. The LPA group had significantly lower SF-12 PCS (p = 0.011) and WOMAC function scores (p = 0.022) at 1 year than the MPA group. Conclusion: There was no significant difference between the MPA and MV approach. The SV approach had some improved long-term outcomes over the MPA aproach (SF-12 and WOMAC), but had significantly lower flexion at 1 year. The LPA group showed inferior outcomes than the MPA group but had more severe valgus preoperative deformity (p = 0.024). Further studies are required to investigate the potential benefit of quadriceps-sparing approaches.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".