Use of MR Imaging in Diagnosing Diabetes-related Pedal Osteomyelitis
Bibliographic record
Abstract
The clinical diagnosis of diabetes-related osteomyelitis relies on the identification and characterization of an associated foot ulcer, a method that is often unreliable. Magnetic resonance (MR) imaging is the modality of choice for imaging evaluation of pedal osteomyelitis. Because MR imaging allows the extent of osseous and soft-tissue infection to be mapped preoperatively, its use may limit the extent of resection. At MR imaging, the simplest method to determine whether osteomyelitis is present is to follow the path of an ulcer or sinus tract to the bone and evaluate the signal intensity of the bone marrow. Combined findings of low signal intensity in marrow on T1-weighted images, high signal intensity in marrow on T2-weighted images, and marrow enhancement after the administration of contrast material are indicative of osteomyelitis. Secondary signs of osteomyelitis include periosteal reaction, a subtending skin ulcer, sinus tract, cellulitis, abscess, and a foreign body. The location of a marrow abnormality is a key distinguishing feature of osteomyelitis: Whereas neuroarthropathy most commonly affects the tarsometatarsal and metatarsophalangeal joints, osteomyelitis occurs distal to the tarsometatarsal joint, in the calcaneus and malleoli. In the midfoot, secondary signs of infection help differentiate between neuroarthropathy and a superimposed infection.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".