Clinical improvement after intraarticular and intraosseous injections of platelet rich plasma combined with hyaluronic acid for knee osteoarthritis. Case series.
Bibliographic record
Abstract
INTRODUCTION: knee osteoarthritis (KOA) is known as the most common form of osteoarthrosis with a 6% prevalence in people over 30 years old, and more than 40% in the population over 70 years old. The use of PRP led to diverse results and this disparity can be attributed to the dissimilar methods of PRP preparation. This study aims to assess the functional effects of intraosseous (IO) and intraarticular (IA) injections of platelet rich plasma (PRP) followed by IA injections of hyaluronic acid (HA). OBJECTIVES: this study aimed to assess the functional effects of intraosseous (IO) and intraarticular (IA) injections of platelet rich plasma (PRP) followed by IA injections of hyaluronic acid (HA), administered 3 and 4 weeks after the initiation of treatment in 33 patients with grade II-III (Ahlback scale) knee osteoarthritis (KOA). MATERIAL AND METHODS: retrospectively, 33 patients were assessed using the Western Ontario and McMaster Universities (WOMAC) osteoarthritis index and visual analogue scale (VAS) score. They were followed-up for 12.92 months on average. Patients were divided into three groups based on age and four groups based on the follow-up period. RESULTS: the pre-operative mean of the WOMAC index was 44.35 ± 20.20 and the post-operative mean was 22.81 ± 17.25 (p < 0.001). The pre-operative and post-operative mean of the VAS scores were 5.79 ± 2.01 and 2.41 ± 1.43 (p < 0.001), respectively. The largest improvement in WOMAC (from 42.86 to 13.69) was observed in the youngest patients (44 to 55 years old) and the largest reduction in VAS (from 6.89 to 2.22) was seen in patients aged 56 to 70 years. CONCLUSION: the combination of IO and IA plasma rich in growth factor (PRGF) treatment with the IA-HA treatment yielded excellent results, diminishing pain and improving motor functionality in patients with KOA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".