Whole-body MRI Imaging Is an Essential Tool in Diagnosing and Monitoring Patients With Sterile Osteomyelitis
Bibliographic record
Abstract
From the first description in 1972 as “subacute and chronic recurrent osteomyelitis” to the currently recognized chronic recurrent multifocal osteomyelitis (CRMO) or chronic nonbacterial osteitis (CNO), diagnosis and monitoring of patients with this disease has been and continues to be a challenge1,2. While the most common presenting symptom is focal bone pain, its waxing and waning nature tends to contribute to the diagnostic odyssey that many patients must endure. Objective changes on examination such as swelling and tenderness over a lesion may not be present or may mimic inflammatory arthritis. Laboratory findings are equally nonspecific, with some patients having a mildly elevated C-reactive protein and/or erythrocyte sedimentation rate, while most other laboratory findings remain normal3. In about one-quarter of patients, a comorbid inflammatory condition such as psoriasis or inflammatory bowel disease, when present, often provides the vital clue to establishing a diagnosis4. However, in those with osseous involvement only, the lack of specific findings makes the diagnosis of CNO challenging, with patients averaging 2 years between initially presenting with symptoms and receiving a diagnosis of CNO5. Given the lack of pathognomonic features in most patients, a high index of suspicion for CNO and close collaboration between clinicians and radiologists are important to making a timely diagnosis. While imaging is essential in establishing a diagnosis of CNO, imaging features of CNO can also be relatively nonspecific. Plain films lack sensitivity, especially early in the disease course and may be completely normal despite significant disease activity. When positive, … Address correspondence to Dr. P. Ferguson, University of Iowa, 200 Hawkins Dr., Iowa City, IA 52240, USA. Email: polly-ferguson{at}uiowa.edu.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".