How to Choose the Appropriate Posterior Slope Angle Can Lead to Good Knee Joint Function Recovery in Total Knee Arthroplasty?
Bibliographic record
Abstract
Xi-Qing Pan, Jin-Hui Liu, Jiang-Li Zhang, An Chai, Feng Li, Lei Shu, Wei Zhao Department of Joint Surgery, Third Hospital of Shijiazhuang, Shijiazhuang, Hebei, People’s Republic of ChinaCorrespondence: Jin-Hui Liu, Department of Joint Surgery, Third Hospital of Shijiazhuang, No. 15 Tiyu South Street, Shijiazhuang, Hebei, 050011, People’s Republic of China, Tel +86 13191872146, Fax +86 31185990628, Email jinhuiauliu6@126.comObjective: In this study, we aim to examine the effects of osteotomy under varying posterior slope angles on knee joint function recovery following knee arthroplasty.Methods: We conducted a retrospective analysis from September 2015 to September 2018 on 240 patients who underwent knee arthroplasty three years previously. The study participants were categorized based on changes in the angle of the posterior slope before and after surgery: Group 1, > 5°; Group 2, 3°– 5°; Group 3, 0°– 3°; Group 4, − 3°– 0°; Group 5, < − 3°. All participants were affected with knee osteoarthritis. The Knee Society Clinical Rating System (KSS) knee function score, Western Ontario and McMaster Universities Arthritis Index (WOMAC) knee function score, Visual Analogue Scale (VAS) pain score, and postoperative complications were measured 3 years after surgery.Results: The level of pain experienced by the patients decreased significantly than before, with pain scores ranging from 1.0– 3.0, and there was a statistical difference between groups (H = 93.400, P
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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) | 0.000 | 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 | 0.001 |
| 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".