Efficacy of Intra-Articular Autologous Platelet Rich Plasma Application in Knee Osteoarthritis
Bibliographic record
Abstract
Objectives: This study aims to evaluate the efficacy of autologous platelet rich plasma applications on pain, functional status, and cartilage regeneration in advanced knee osteoarthritis. Patients and methods: A total of 82 patients (13 males, 69 females; mean age 63.5±9.3 years; range 40 to 88 years) with chronic knee pain for the last one year, who had grade 3-4 knee osteoarthritis according to Kellgren-Lawrence Scale and visual analog scale value of higher than 5, who did not receive physical therapy for the last six months, and did not respond to treatment despite use of nonsteroidal antiinflammatory drugs and analgesics at least for the last three months were enrolled in the study. Totally 103 knee joints of 82 patients were applied intra-articular platelet rich plasma in the beginning, and first and second weeks of treatment. Platelet rich plasma was applied to both knees in 20 patients. Patients were evaluated clinically with visual analog scale, functionally with Western Ontario and McMaster Universities Osteoarthritis Index, and six-minute walk test before application and after application at first and second weeks, and third and sixth months. Cartilage thicknesses were measured by ultrasound before treatment and at third and sixth months after treatment. Obtained results were compared. Results: Compared to values before treatment, patients’ visual analog scale values were significantly decreased at third and sixth months after treatment (p
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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 | 0.000 |
| 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) | 0.001 | 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".