EFFECTS OF PLATELET-RICH PLASMA INJECTIONS ON OSTEOARTHRITIC PATIENTS
Bibliographic record
Abstract
Objective: Various treatment options are available for knee osteoarthritis such as medical treatment with NSAID, conservative management with platelet-rich plasma (PRP) and corticosteroids. We have done this prospective study to know the use and safety of platelet- rich plasma (PRP) injections in knee osteoarthritis (KOA) patients. We know platelet rich plasma (PRP) clinical and functional outcome in knee osteoarthritis (KOA) by doing this study and using the available literature. Methods: This prospective study consisted of a total number of 96 patients suffering from knee osteoarthritis. Both males and females are included. Intra-articular injection of platelet rich plasma (PRP) was given in sterile conditions and clinical and functional outcomes were analyzed with Western Ontario and McMaster University Arthritis Index (WOMAC), Visual Analogic Scale (VAS), and Knee Society score (KSS). This study is done in a tertiary care institute during the study period.Results: Most patients were females aged>40 years with knee osteoarthritis. The injections of platelet rich plasma (PRP) showed results at three, six and twelve months follow-up showed significantly reduced WOMAC scores, Visual Analogic Scale (VAS) and Knee Society score (KSS). No complications were observed during the follow-up period. Conclusion: The results confirm the efficacy of the PRP injections on Knee osteoarthritis, suggesting that decreasing pain was obtained one month after injection, with the best results observed after 12 months—however, a more extensive study group. Follow-up is required for a prolonged period to assess the efficacy of PRP injection.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".