A study on the effectiveness of visco-supplementation in osteoarthritis knee
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis (OA) is a degenerative disease of synovial joints; recently, it is also termed osteoarthrosis. High-molecular-weight hyaluronic acid (HA) has a better increase in fluid retention within the joint and stronger anti-inflammatory effect.OBJECTIVES: The objective was to assess the efficacy of intra-articular (IA) HA in primary OA of knee joint based on clinical outcome with visual analog score (VAS) and the Western Ontario and McMaster Universities OA Index (WOMAC) score and to determine the safety of IA HA in primary OA of knee joint.SUBJECTS AND METHODS: A single-group, prospective interventional study was conducted from November 2017 to May 2019 in a tertiary care hospital. A total of 36 patients with Grades I and II of Kellegren–Lawrence radiological grading were included in the study. IA injection of HA was given to these patients and were assessed using VAS and WOMAC scores.RESULTS: The mean VAS scores improved from preinjection score of 9.03 ± 0.94 to 2.61 ± 2.15 at 6-month follow-up. In addition, the mean WOMAC scores improved from 81.14 ± 6.43 to 35.81 ± 13.44 at 6-month post-IA HA injection.CONCLUSIONS: This study observed that IA injection of HA is a reliable, productive, efficient, and safe mode of treatment of Grade I and II OA knee, which also delays the disease and reduces the need for surgical intervention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".