Comparison of Clinical Signs Associated with Lumbar Spine in Patients with Simultaneous Knee Osteoarthritis and Lumbar Spine Osteoarthritis before and after Knee Arthroplasty
Bibliographic record
Abstract
Background: Knee osteoarthritis (OA) and low back pain (LBP) are common and co-occur in the elderly. The LBP in patients who are candidates for knee arthroplasty affects the outcome and prognosis after surgery. In this study, we investigated the LBP in patients with simultaneous knee and lumbar spine OA after total knee arthroplasty. Methods: In this cross-sectional study, 41 candidates for knee arthroplasty suffering from LBP were included. Demographic and visual analogue scale (VAS) questionnaires for LBP and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire for knee pain and function were completed by patients before surgery. Patients were graded according to VAS index. They were followed up for at least six months to two years. Results: The mean age of 41 patients was 64.30 ± 6.46 years. The mean of the preoperative VAS index was 5.15 ± 2.75, while postoperative VAS decreased to 4.34 ± 3.53 (P = 0.024). Of the total number of patients in preoperative evaluation, 24.4% were in low grades based on the VAS index, followed by moderate (41.5%) and severe (34.1%) grades. The greatest improvement in the VAS index was related to those in mild and moderate grades before surgery. The mean preoperative WOMAC index was 55.1 ± 23.7, while it was postoperatively reduced to 42.9 ± 30.6 (P < 0.001). Postoperative WOMAC was found to be correlated with postoperative VAS (P = 0.004). Conclusion: In patients with mild to moderate LBP and knee OA, their back pain would improve if they had knee arthroplasty. However, in patients with severe LBP and knee OA, the spine should be examined further.
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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 | 0.002 |
| 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 | 0.001 | 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".