Clinical Outcomes Comparison in Administration of Secretome vs Hyaluronic Acid in Patients with Knee Osteoarthritis Kellgren-Lawrence Grade I-III
Bibliographic record
Abstract
Background: The prevalence of OA in Indonesia is predicted to reach more than 20% of the population aged over 60 years in 2050, with a high risk of disability. So far, early treatment of osteoarthritis in the form of administering hyaluronic acid has not been completely satisfactory and tends to be progressive until ending in more invasive operative therapy. This study aimed to compare the clinical outcomes between secretome injection and hyaluronic acid in patients with Kellgren-Lawrence grade I-III knee osteoarthritis. Subjects and Method: This was a single-blind experimental study. This study was conducted in the orthopedic polyclinic at General Hospital Prof. Dr. I.G.N.G. Ngoerah Bali. A total sample of 36 knee osteoarthritis patients was selected using permuted block sampling with randomization. The sample was divided into two groups, (1) secretome (intervention group) and (2) hyaluronic acid (control group). The independent variables were secretome injection and hyaluronic acid injection. The dependent variable was pain. Pain was measured using Western Ontario and McMaster University (WOMAC), Knee Osteoarthritis Outcome Score (KOOS), Visual Analog Score (VAS), and Patient-Reported Outcome Measure (PROM). Results: Based on gender, there are more women than men and the right foot is more affected at 58.3%. Functional score parameters in each secretome and hyluronic acid group were compared between the 3rd and 6th months of follow-up. Pain in the hyaluronic acid group was lower than in the secretome group. Conclusion: Hyaluronic acid has better effect in pain reduction than secretome. Further studies could explore the underlying mechanisms and potential long-term effects to better understand these differences in pain outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".