Efficacy of Intra-Articular Ozone Versus Hyaluronic Acid In Patients of Knee Osteoarthritis
Bibliographic record
Abstract
Objective: To determine the analgesic efficacy of ozone gas versus hyaluronic acid solution in knee osteoarthritis patients. Study Design: Quasi-experimental study. Place and duration of study: Department of Pain Medicine, Combined Military Hospital, Rawalpindi Pakistan, from Jun to Dec 2020. Methodology: Seventy patients suffering from knee osteoarthritis fulfilling the inclusion criteria were included in this study and were randomly assigned to two equal groups to undergo intra-articular knee injection using either Hyaluronic Acid (Group-H) or Ozone (Group-O). Improvement in the numeric rating scale (NRS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scale were recorded 1, 3 and 6 months after the procedure. Results: In both groups, the pain score (Group-H Pre-procedure NRS =7.66±0.87 vs. Post-Procedure 6 month NRS = 4.74±0.70, Group-O Pre-procedure NRS = 7.86±0.88 vs. Post-Procedure 6 month NRS = 4.46±0.92) and WOMAC (Group-H Pre-procedure =77.60±7.93 vs. Post-Procedure 6 month =40.31±6.81, Group-O Pre-procedure=75.54±9.40 vs. Post-Procedure 6 month =38.37±8.98) score improved. However, the NRS pain score (p-value = 0.21) and patient WOMAC score (p-value = 0.31) were not significantly different between groups. Conclusion: Neither Hyaluronic Acid nor Ozone appears superior in decreasing pain scores or physical limitations, particularly for knee osteoarthritis.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".