The Efficacy of Platelet-Rich Plasma Injection Therapy in Obese versus Non-Obese Patients with Knee Osteoarthritis: A Comparative Study
Bibliographic record
Abstract
Background/Objectives: Obesity is a common comorbidity in knee osteoarthritis (KOA) patients. Platelet-Rich Plasma (PRP) injection therapy may mitigate KOA. To further clarify potential patient selection for PRP injection therapy, we compared the outcomes in patients with different body mass index (BMI). Methods: A total of 91 patients with mild to moderate KOA were treated with three intra-articular PRP injections at 10 to 14-day intervals. Range of motion (ROM), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and Visual Analogue Scale (VAS) were documented before and after the injections at 15 days, 6 months, 12 months, and at the last follow-up. Outcomes were compared between patients with a BMI over 30 kg/m2 (obese, n = 34) and under 30 kg/m2 (non-obese, n = 57). Results: Significant difference during the follow-up was detected in WOMAC score at the last follow-up favouring BMI under 30 group [17.8 ± 18.8 versus 10.5 ± 11.7, p = 0.023]. The odds ratio (OR) in BMI over 30 kg/m2 group for total knee arthroplasty was 3.5 (95% CI 0.3–40.1, p = 0.553), and OR for any arthroplasty was 7.5 (95% CI 0.8–69.8, p = 0.085) compared to non-obese patients. Conclusions: Obese patients benefitted from PRP injections in KOA but there is a minimal difference favouring non-obese patients in symptom alleviation in follow-up stages after 12 months. The risk of arthroplasty is higher for obese KOA patients.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".