Risk factors for knee instability after total knee arthroplasty surgery
Bibliographic record
Abstract
Objective To screen and evaluate the risk factors for knee instability after total knee arthroplasty on. Methods Clinical data of 136 patients who was diagnosed with knee osteoarthritis and underwent unilateral total knee arthroplasty in our department during January 2015 and December 2018 were collected and analyzed retrospectively. They were divided into knee stability and instability groups according to their conditions before and in 1 year after surgery. Knee pain and dysfunction grades of Western Ontario and McMaster Universities Arthritis Index (WOMAC), stair-climbing power, 25-meter straight-line walk time, and knee range of motion were recorded and statistically analyzed. Results Among the 136 cases, 77.9% had preoperative knee instability, and 20.6% of them retained instability in 1 year after surgery. The subjects with retaining knee instability after surgery had significantly serious WOMAC pain and activity limitations compared to the patients without instability (7.1±3.4 vs 3.0±2.5, 18.9±11.2 vs 8.4±8.6, P < 0.01). Multivariate prediction model analysis predicted that comorbidity score (>6 points) (P < 0.01), low stair climbing power (< 150 W) (P < 0.01), moderate-severe pain (>7 points) (P < 0.01) and older age (>60 years) (P < 0.01) were independent risk factors for persistent knee instability after surgery. Conclusion Knee instability has a high incidence before and after total knee arthroplasty and is associated with the potential risk factors of knee pain, activity limitation and stair climbing power.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".