Quantitative Radiographic Progression of Joint Space Narrowing in Medial and Lateral Compartment Knee Osteoarthritis After Intraarticular Platelet-Rich Plasma Injection
Bibliographic record
Abstract
INTRODUCTION: Knee osteoarthritis (KOA) is a common pathology of the knee. Intraarticular injection of platelet-rich plasma (PRP) can be used as a nonoperative therapy to improve symptoms. The impact of PRP on structural derangements of KOA has not been widely studied. Although radiography is an appropriate imaging modality for initial evaluation and follow-up, many published studies evaluating for structural change in KOA use magnetic resonance imaging or ultrasound. OBJECTIVE: The aim of our study was to evaluate for change in osteoarthritis severity and joint space width (JSW) on radiography in patients with KOA treated with PRP. METHODS: Included study patients had pre-injection and post-injection posterior-anterior flexion radiography obtained. Severity of osteoarthritis was assessed using the Kellgren-Lawrence grade, and JSW was measured. RESULTS: Sixteen patients with 20 PRP-treated knees (13 medial-compartment-predominant and seven lateral-compartment-predominant KOA) were included in the analysis. Severity grade did not change in 18 of the 20 knees but worsened in two knees at follow-up. A median change of -0.1 mm in JSW after PRP injection at a median follow-up of 11.5 months was seen in both lateral- and medial-compartment-predominant knees, which is less than the expected annual rate of joint space narrowing in untreated KOA. CONCLUSION: Our study was underpowered and therefore was not able to demonstrate a significant structural change following PRP treatment using a radiographic comparison model. Larger studies are needed to provide further assessment on the structural impact of PRP injection in 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".