Varus tibial alignment is associated with greater tibial baseplate migration at 10 years following total knee arthroplasty
Bibliographic record
Abstract
PURPOSE: To examine implant migration and articular behavior of primary total knee arthroplasty (TKA) at 10 years after index surgery and correlate to implant alignment. METHODS: Thirty-five patients underwent a cemented posterior stabilized total knee arthroplasty with a surgical objective of neutral alignment and were enrolled in a long-term radiostereometric analysis (RSA) study. At 10 years after surgery, patients were analyzed for implant migration using RSA as well as radiographic assessment of articular behavior at four positions of knee flexion. Implant position and alignment was measured on full-length radiographs. Patient demographics and reported outcomes were also collected. RESULTS: No difference between patient demographics or patient-reported outcomes were found. When categorized into neutral and varus groupings, no difference in migration was present. If alignment was considered as a continuous variable, there was no correlation between overall leg alignment and migration, however, migration increased with an increasing varus tibial alignment. Although contact location did not differ between neutral and varus groups through a range of motion, condylar liftoff was much more common in the varus group, of which all were lateral liftoff. CONCLUSIONS: Increased tibial varus results in increased implant migration. Overall varus limb alignment is correlated with isolated lateral compartment liftoff, and liftoff occurs more commonly than in neutral aligned knees. The increased migration and liftoff raise concerns about the longevity of malaligned total knee replacements. If a goal of overall varus limb alignment is desired for TKA, the tibia should remain neutral. LEVEL OF EVIDENCE: 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".