Clinical Efficacy of 2-Needle Joint Lavage for Osteoarthritis-Related Knee Pain and Predictors of Response Based on Knee MRI Osteoarthritis Knee Score: A Medical Records Review Study
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (OA) is notoriously difficult to treat. Pain is the key symptom for patients to seek medical attention. This study aimed to evaluate the therapeutic efficacy of joint lavage (JL) for OA-related knee pain and to explore the knee pathological changes detected by magnetic resonance imaging that may affect the prognosis of patients who received JL. METHODS: Eighty-two hospitalized patients who were diagnosed with knee OA and received JL in our department were finally enrolled in this study. The patients' clinical data including Numeric Rating Scale (NRS), Western Ontario and McMaster Osteoarthritis Index pain subscale, analgesic medication usage, adverse events, and magnetic resonance imaging data of the affected knee joint scored by the MRI Osteoarthritis Knee Score were recorded and analyzed. RESULTS: The NRS scores significantly decreased after JL and remained steady until 6 months ( p < 0.001). The Western Ontario and McMaster Osteoarthritis Index pain scores and the percentage of patients who needed analgesic medication significantly decreased at 6 months compared with baseline ( p < 0.001). At 6 months after JL, 51 of the 82 patients experienced ≥50% improvement in their NRS scores (effective). Multivariate binary logistic regression analysis revealed that duration of pain (odds ratio [OR], 1.022; 95% confidence interval [CI], 1.003-1.042; p = 0.024), bone marrow lesion score (OR, 1.221; 95% CI, 1.028-1.450; p = 0.023), and cartilage loss score (OR, 1.272; 95% CI, 1.021-1.585; p = 0.032) significantly influenced the therapeutic efficacy of JL. CONCLUSIONS: JL treatment can significantly alleviate the OA-related knee pain in at least 6 months. JL tends to provide limited benefit for patients with long duration of pain, serious bone marrow lesions, and severe cartilage loss.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".