Comparison of platelet-rich plasma vs hyaluronic acid injections in patients with knee osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (KOA) is a progressive joint disease involving intraarticular and periarticular structures. In recent years, there has been increasing interest in the use of autologous growth factors, such as intraarticular injections of platelet-rich plasma (PRP), to treat KOA. It is necessary to update the research and reevaluate the efficacy and safety of PRP to provide up-to-date evidence for KOA management. Therefore, we provide a protocol for a systematic review of PRP for KOA. METHODS: The aim of this study was to retrieve papers on the topic of PRP treatment for KOA in electronic databases including PubMed, Embase, and the Cochrane Library. The search will include studies that were published from the time the databases were established until April 2018. The entire process will include study selection, data extraction, risk of bias assessment, and meta-analyses. RESULTS: The literature will provide a high-quality analysis of the current evidence supporting PRP for KOA based on various comprehensive assessments including the Western Ontario and McMaster Universities Osteoarthritis Index, visual analog scale scores, International Knee Documentation Committee scores, Lequesne index scores, and adverse events. CONCLUSION: This proposed systematic review will provide up-to-date evidence to assess the effect of PRP treatment for patients with KOA. PROSPERO REGISTRATION NUMBER: CRD42018108825.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".