Evaluating bone marrow oedema patterns in musculoskeletal injury
Bibliographic record
Abstract
MRI is a common tool in the evaluation of musculoskeletal injury that allows the clinician to pinpoint specific pathologies. The patient's history and physical exam play a critical role in the diagnosis of sports injuries, however, complementary imaging can play an important role in determining the nature and extent of injury. With the widespread use of MRI, attention has focused on the signals generated following injury. In particular, bone marrow oedema (BME) patterns can be used to aid in the diagnosis of musculoskeletal injury. In this pictorial essay, the authors will demonstrate common patterns of BME that accompany a wide range of musculoskeletal injuries. It is expected that by the end of this article, the reader will be able to (1) recognise BME is a phenomenon observed on MRI following sports injury; (2) recognise typical patterns of BME; (3) understand the relationship of oedema to the type of injury and (4) in the presence of oedema, understand other co-existing injuries that ultimately may have an impact on management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".