THE EFFECTIVENESS OF INTRA-ARTICULAR INJECTIONS HYALURONIC ACID AND CORTICOSTEROIDS IN THE TREATMENT OF PATIENTS WITH KNEE OSTEOARTHRITIS SYMPTOMS
Bibliographic record
Abstract
Introduction: Knee osteoarthritis (OA) is a degenerative condition that is caused by the gradual wear and tear of the cartilage that cushions the bones of the knee. This study will find out the effectiveness of intra-articular hyaluronic acid (IAHA) and intra-articular corticosteroids (IACS) in the management of knee OA. Material & Methods: A randomized control trial was conducted in Hayatabad Medical Complex Peshawar and Pak Military Hospital Rawalpindi from March 2019 to June 2021. Total one eighty-two patients were randomly assessed in the study. Treatment group A received intra-articular hyaluronic acid (IAHA), while treatment group B received intra-articular corticosteroids for three months. This study has used Western Ontario and McMaster Universities Arthritis Index (WOMAC) to evaluate the effectiveness of treatment in both groups of knee OA. Results: The study participants mean age was 56.344 ± 7.25. Out of 182 patients, 2 of the patients reported as “loss to follow up” from IACS Tx group b. Treatment groups include 95 (52.7%) female patient while 85 (47.22%) were male patients. 96 (53.33%) received intra-articular hyaluronic acid (IAHA), while 84 (46.66%) received intra-articular steroids (IACS). The pre- and post-treatment mean difference in WOMAC scores in the hyaluronic acid group was 5.52 ± 4.756, while that in the corticosteroid group was 9.495 ± 1.24 (p value < 0.01). Conclusion: Intra-articular steroids (IACS) and hyaluronic acid (IAHA) alone reduces OA symptoms and pain significantly. Yet intra-articular steroids are more effective than intra-articular hyaluronic acid in reducing symptoms of OA at knee.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".