The relationship between MRI-detected hip abnormalities and hip pain in hip osteoarthritis: a systematic review
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) is increasingly used in the classification and evaluation of osteoarthritis (OA). Many studies have focused on knee OA, investigating the association between MRI-detected knee structural abnormalities and knee pain. Hip OA differs from knee OA in many aspects, but little is known about the role of hip structural abnormalities in hip pain. This study aimed to systematically evaluate the association of hip abnormalities on MRI, such as cartilage defects, bone marrow lesions (BMLs), osteophytes, paralabral cysts, effusion-synovitis, and subchondral cysts, with hip pain. We searched electronic databases from inception to February 2024, to identify publications that reported data on the association between MRI features in the hip joint and hip pain. The quality of the included studies was scored using the Newcastle-Ottawa Scale (NOS). The levels of evidence were evaluated according to the Cochrane Back Review Group Method Guidelines and classified into five levels: strong, moderate, limited, conflicting, and no evidence. A total of nine studies were included, comprising five cohort studies, three cross-sectional studies, and one case-control study. Moderate level of evidence suggested a positive association of the presence and change of BMLs with the severity and progress of hip pain, and evidence for the associations between other MRI features and hip pain were limited or even conflicting. Only a few studies with small to modest sample sizes evaluated the association between hip structural changes on MRI and hip pain. BMLs may contribute to the severity and progression of hip pain. Further studies are warranted to uncover the role of hip MRI abnormalities in hip pain. The protocol for the systematic review was registered with PROSPERO ( https://www.crd.york.ac.uk/PROSPERO/ , CRD42023401233).
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".