Similar results with kinematic and mechanical alignment applied in total knee arthroplasty
Bibliographic record
Abstract
PURPOSE: This meta-analysis compared the results of kinematic alignment (KA) and mechanical alignment (MA) applied in total knee arthroplasty (TKA). METHODS: Randomized controlled trials and cohort studies comparing functional, radiological, and perioperative results and complications in TKA with KA and MA were collected from databases and included in the analysis. RESULTS: Nine trials were included. KA showed a better performance in terms of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) (mean difference [MD] = - 9.06, 95% confidence interval [CI] - 14.69, - 3.42) and Oxford knee score (OKS) (MD = 4.72, 95% CI 0.24, 9.21); however, the Knee Society score (KSS), knee injury and osteoarthritis outcome score (KOOS), EuroQoL 5-dimension questionnaire (ED-5D), range of motion, and complications were similar for KA and MA (n.s.). KA resulted in slightly more varus alignment in the tibia [mechanical medial proximal tibial angle (mMPTA) MD = - 2.45, 95% CI - 2.89, - 2.01) and more valgus alignment in the femur (mLDFA MD = - 2.06, 95% CI - 2.48, - 1.65) than MA (P < 0.05), but showed similar results in terms of the joint line orientation angle (JLOA) (MD = 0.54, 95% CI - 2.59, 3.66), hip-knee-ankle angle (HKA), anatomical knee angle (AKA), femoral flexion-extension angle (FFA), and tibial slope (TS). The preoperative results, including the incision length, hospital stay, and changes in hemoglobin, were also similar. CONCLUSION: KA achieved functional, radiological, and perioperative results similar to those of MA and did not increase the complication rate. KA is an acceptable and satisfactory method for application in TKA. LEVEL OF EVIDENCE: 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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.034 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".