The influence of posterior tibial slope on the mid-term clinical effect of medial-pivot knee prosthesis
Bibliographic record
Abstract
Abstract Objective To evaluate the effect of posterior tibial slope (PTS) on the mid-term clinical outcome following a medial-pivot (MP) prosthesis. Method Two hundred thirty-three patients from The Affiliated Hospital of Qingdao University, who had undergone a total knee arthroplasty (TKA) with MP prosthesis between January 2015 and December 2015, were retrospectively included in this study. They were divided into 3 groups according to postoperative PTS: A ≤ 5°; B 5-7°; and C ≥ 7°. Multiple assessments were made on the patient postoperatively and recorded in the three groups, the measurements of this study included: the range of motion (ROM), knee scoring system (KSS), Western Ontario and McMaster universities osteoarthritis index (WOMAC), posterior condylar offset (PCO), joint line height, and postoperative complications. Results The average post-operative ROM for groups B and C were 108° and 110° respectively; this was significantly higher than that of group A (98°, P < 0.001). The WOMAC scores of patients in group C were significantly lower than those in groups A and B (P < 0.05). However, there were no significant differences in KSS, PCO, and joint line height among the 3 groups (P > 0.05). Only 2 cases of postoperative complications occurred in group C, these were ameliorated after operation. Conclusion With an increase to PTS, the postoperative ROM can be significantly increased for the patient. However, the knee joint function will not be significantly improved, and the stability of knee joint will not be affected when within the limits of appropriate PTS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".