The Effects of Resection Technique on Implant Migration in Single Radius Posterior-Stabilized Total Knee Replacement
Bibliographic record
Abstract
Abstract The purpose of the present study was to measure the effects of gap balancing and resection techniques on migration of a single total knee replacement implant design. A total of 23 patients (24 knees) were recruited on referral to either a surgeon performing gap balancing or a surgeon performing measured resection and followed prospectively. All patients received a fixed bearing, posterior stabilized total knee replacement implant of a single radius femoral component design with cement fixation, and all aspects of care outside of resection technique were identical. Patients underwent radiostereometric analysis (RSA) at 2 weeks (baseline), 6 weeks, 3 months, 6 months, 1 year, and 2 years. Migration of the tibial and femoral components was compared between groups. Tibial component migration was greater at 2 years in the gap balancing group (mean difference = 0.336 mm, p = 0.036), but there was no difference at 1 year. One measured resection and three gap balancing tibial components demonstrated continuous migration > 0.2 mm between years 1 and 2. There was no difference in femoral component migration. Small differences in tibial component migration were found between the gap balancing and measured resection techniques. However, comparing the migration to established predictive thresholds for long-term loosening risk, implants performed with both techniques were found to have equally low revision risk.
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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 | 0.005 |
| 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".