Are patients with knee osteoarthritis aware that platelet-rich plasma is a treatment option?
Bibliographic record
Abstract
Osteoarthritis (OA) is a prevalent joint disease, particularly affecting the knees. This condition is often managed through various treatments, including intra-articular injections such as corticosteroids (CS), hyaluronic acid (HA), and platelet-rich plasma (PRP). PRP has shown promising outcomes in recent studies although it does lack strong endorsement in some clinical guidelines due to inconsistent results and lack of standardized results. This study was conducted to assess patient awareness and the frequency of PRP offered for the treatment of knee OA, compared to CS and HA. In a cross-sectional study, 46 knee OA patients were surveyed regarding their knowledge and experiences of CS, HA, and PRP injections. The questionnaires were administered between September 2022 and February 2023. Additionally, the study evaluated the severity of patients knee OA, using the Western Ontario and McMaster Universities Arthritis Index, and gathered demographic information from the participants. CS injections were offered to 93.5%, and 100% of participants had previously heard of this type of injection. HA injections were offered to 37%, and 65.9% of participants had heard of them. PRP was offered to 2%, and 6.5% had ever heard of it. This study underscores the limited awareness and utilization of PRP among knee OA patients. Patients and physicians need to be more informed of all of the treatment options available for knee OA, especially orthobiologics such as PRP. Future research in larger, diverse populations is needed.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| 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".