Knee MRI biomarkers associated with structural, functional and symptomatic changes at least a year from ACL injury - A systematic review
Bibliographic record
Abstract
Introduction: Osteoarthritis (OA) results from various aetiologies, including joint morphology, biomechanics, inflammation, and injury. The latter is implicated in post-traumatic OA, which offers a paradigm to identify potential biomarkers enabling early identification and intervention. This review aims to describe imaging features associated with structural changes or symptoms at least one year following injury. Methodology: A systematic review was conducted using PRISMA guidance, prospectively registered on PROSPERO (CRD42022371838). Three independent reviewers screened titles and abstracts, followed by full-texts, performed data extraction, and risk of bias assessments (Newcastle-Ottawa Scale). Inclusion criteria included imaging studies involving human participants aged 18-45 who had sustained a significant knee injury at least a year previously. A narrative synthesis was performed using synthesis without meta-analysis methodology. Results: Six electronic databases and conference proceedings were searched, identifying 11 studies involving 776 participants. All studies included participants suffering an anterior cruciate ligament (ACL) injury and utilised MRI. Different, and not directly comparable, techniques were used. MRI features could be broadly divided into structural, including joint position and morphology, and compositional. Promising biomarkers for diagnosing and predicting osteoarthritis include T1rho and T2 relaxation time techniques, bone morphology changes and radiomic modelling. Discussion: As early as 12 months after injury, differences in tibia position, bone morphology, presence of effusion and synovitis, and cartilage/subchondral bone composition can be detected, some of which are linked with worse patient-reported or radiological progression. Standardisation, including MR strength, position, sequence, scoring and comparators, is required to utilise clinical and research OA imaging biomarkers fully.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".