Effect of Polyethylene Component Thickness on Range of Motion and Stability in Primary Total Knee Arthroplasty
Bibliographic record
Abstract
Total knee arthroplasty (TKA) is a common procedure with good survivorship and functional results. Optimal results are dependent on proper osseous cuts and soft tissue balancing. Soft tissue tensioning via the polyethylene spacer thickness is an important component of soft tissue balancing. Increased thickness increases soft tissue tension and, therefore, has the potential to increase stability but decrease range of motion (ROM). Decreased polyethylene thickness may decrease soft tissue tension and has the potential to increase ROM but decrease stability. Using computer-based navigation, the intraoperative effect of increasing and decreasing polyethylene thickness in 1-mm increments on ROM and coronal stability throughout the ROM of 35 patients was examined. It was found that increasing the polyethylene thickness by 1-mm increments had a statistically significant impact on the ability to achieve full extension but had no impact on flexion. Increased polyethylene thickness decreased coronal plane motion. Coronal plane laxity increased with increased flexion irrespective of polyethylene thickness. In this patient cohort, lateral laxity became >1° when the knee was flexed. However, medial structures prevented valgus angulation of >1° in all scenarios except when the polyethylene was diminished by 2 mm. Changes in polyethylene thickness had an impact on the ability to gain full extension and coronal plane motion.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".