A prospective study of intra-articular injections of platelet rich plasma in early osteoarthritis knee joint
Bibliographic record
Abstract
Background: Platelet-rich plasma (PRP) is a natural concentrate of autologous blood growth factors experimented in different fields of medicine in order to test its potential to enhance tissue regeneration. The aim of our study is to study the effects of intra articular injections of autologous “Platelet Rich Plasma“ in the osteoarthritis of knee joint and to assess the functional outcome after injecting platelet rich plasma in osteoarthritic knee joints and compare the results with standard studies and draw conclusions. Methods: This is a prospective study done on 161 Kellgren-Lawrence radiological grade I and grade II knee joints, between August 2013 and March 2016 which, were treated with PRP intra-articular injections. The procedure consisted of injection of 8 ml platelet concentrate into the knee joint with an 18- gauge needle without local anaesthetic. Patients were assessed with WOMAC (Western Ontario McMaster Universities Arthritis Index) scoring pre injection of PRP and post injection period of 1 month and 6 months. A reduction in WOMAC score is suggestive of improvement in the patient’s condition. Results: There is a significant improvement in WOMAC score of all the patients and the results sustained for more than 6 months. Though there is clinically better results are seen in grade I knee joints than grade II knee joints, these results are statistically not significant. Conclusions: Osteoarthritis (Grade I and Grade II) of Kellgren Lawrence radiological grading does give relief from pain, stiffness and improves functionality without any major side effects and can be recommended as a viable modality of treatment.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".