Clinical relevance of MRI knee abnormalities in Australian rules football players: a longitudinal study
Bibliographic record
Abstract
BACKGROUND/AIM: The clinical relevance of MRI knee abnormalities in athletes is unclear. This study aimed to determine the prevalence of MRI knee abnormalities in Australian Rules Football (ARF) players and describe their associations with pain, function, past and incident injury and surgery history. METHODS: 75 male players (mean age 21, range 16-30) from the Tasmanian State Football League were examined early in the playing season (baseline). History of knee injury/surgery and knee pain and function were assessed. Players underwent MRI scans of both knees at baseline. Clinical measurements and MRI scans were repeated at the end of the season, and incident knee injuries during the season were recorded. RESULTS: MRI knee abnormalities were common at baseline (67% bone marrow lesions, 16% meniscal tear/extrusion, 43% cartilage defects, 67% effusion synovitis). Meniscal tears/extrusion and synovial fluid volume were positively associated with knee symptoms, but these associations were small in magnitude and did not persist after further accounting for injury history. Players with a history of injury were at a greater risk of having meniscal tears/extrusion, effusion synovitis and greater synovial fluid volume. In contrast, players with a history of surgery were at a greater risk of having cartilage defects and meniscal tears/extrusion. Incident injuries were significantly associated with worsening symptoms, BML development and incident meniscal damage. CONCLUSIONS: MRI abnormalities are common in ARF players, are linked to a previous knee injury and surgery history, as well as incident injury but do not dictate clinical symptomatology.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".