Comparative evaluation of efficacy of autogenous platelet rich plasma versus visco supplementation in treatment of early osteoarthritis of knee
Bibliographic record
Abstract
Background: Osteoarthritis is a very common chronic degenerative disease most commonly affecting the knee joints. In present study we compared the efficacy of autogenous platelet rich plasma (PRP) versus visco supplementation in treatment of early osteoarthritis of knees. Methods: 30 patients (56 knees) were registered and divided into two groups. Out of which PRP in 28 knees and visco supplementation in 28 knees injected during. Visual analogue scale (VAS) and Western Ontario and McMaster Universities Arthritis Index (WOMAC) scores were measured. These scores were measured at first visit, 6 weeks, 12 weeks and at 24 weeks. Results: All registered patients were randomized in two groups. Group I (total 16 patients and 28 knees) for intraarticular PRP injection and group II (total 14 patients and 28 knees) for intraarticular viscosupplement injection. Out of 28 knees of group I; 12 (42.85%) knees belonged to grade II and 16 (57.15%) of grade III. Out of 28 knees of group II; 15 (53.57%) knees of grade II and 13 (46.45%) of grade III. None of the knees belonged to grade I and grade 0. There was significant difference in outcome of two treatment groups (p<0.05) at 24 weeks. Conclusions: Treatment with PRP showed a significantly better clinical outcome compared with viscosupplemention at 24 weeks follow up. Although patients achieved lower WOMAC and VAS scores in PRP group at 6 and 12 weeks follow up that was statistically insignificant. We conclude that long term results of PRP are better than viscosupplementation.
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 | 0.000 | 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".