The Role of Polyethylene Design on Postoperative TKA Flexion: An Analysis of 1534 Cases
Bibliographic record
Abstract
UNLABELLED: The range of motion after TKA depends on many patient, surgical technique, and implant factors. Recently, high-flexion designs have been introduced as a means of ensuring or gaining flexion after TKA. We therefore evaluated factors affecting postoperative flexion to determine whether implant design influences longterm flexion. We prospectively collected data on patients receiving a primary Genesis II total knee replacement with a minimum of 1-year followup (mean, 5.4 years; range, 1-13 years). We recorded pre- and postoperative outcome measures, patient demographics, and implant design (cruciate retaining [CR, n = 160], posterior stabilized [PS, n = 1177], high-flex posterior stabilized [HF-PS, n = 197]). Backward stepwise linear regression modeling identified the following factors affecting postoperative flexion: preoperative flexion, gender, body mass index, and implant design. Independent of gender, body mass index, and preoperative flexion, patients who received a HF-PS and PS design implant had a mean of 8 degrees and 5 degrees more flexion, respectively, than those who received a CR implant. Patients with low flexion preoperatively (<100 degrees) were more likely to gain flexion, whereas those with high flexion preoperatively (>120 degrees) were most likely to maintain or lose flexion postoperatively. Controlling for implant design, patients with high flexion preoperatively (>120 degrees) were more likely to gain flexion with the HF-PS design implant (HF-PS = 32.0%; PS = 15.1%; CR = 4.5%). LEVEL OF EVIDENCE: Level II, prognostic study. See Guidelines for Authors for a complete description of levels of evidence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".