Does a mobile-bearing, high-flexion design increase knee flexion after total knee replacement?
Bibliographic record
Abstract
This prospective randomised controlled double-blind trial compared two types of PFC Sigma total knee replacement (TKR), differing in three design features aimed at improving flexion. The outcome of a standard fixed-bearing posterior cruciate ligament-preserving design (FB-S) was compared with that of a high-flexion rotating-platform posterior-stabilised design (RP-F) at one year after TKR. The study group of 77 patients with osteoarthritis of the knee comprised 37 men and 40 women, with a mean age of 69 years (44.9 to 84.9). The patients were randomly allocated either to the FB-S or the RP-F group and assessed pre-operatively and at one year post-operatively. The mean post-operative non-weight-bearing flexion was 107° (95% confidence interval (CI) 104° to 110°)) for the FB-S group and 113° (95% CI 109° to 117°) for the RP-F group, and this difference was statistically significant (p = 0.032). However, weight-bearing range of movement during both level walking and ascending a slope as measured during flexible electrogoniometry was a mean of 4° lower in the RP-F group than in the FB-S group, with 58° (95% CI 56° to 60°) versus 54° (95% CI 51° to 57°) for level walking (p = 0.019) and 56° (95% CI 54° to 58°) versus 52° (95% CI 48° to 56°) for ascending a slope (p = 0.044). Further, the mean post-operative pain score of the Western Ontario and McMaster Universities Osteoarthritis Index was significantly higher in the RP-F group (2.5 (95% CI 1.5 to 3.5) versus 4.2 (95% CI 2.9 to 5.5), p = 0.043). Although the RP-F group achieved higher non-weight-bearing knee flexion, patients in this group did not use this during activities of daily living and reported more pain one year after surgery.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".