The Role of a Virtual Noncalcium Dual-Energy CT Application in the Detection of Bone Marrow Edema in Peripheral Osteomyelitis
Bibliographic record
Abstract
Purpose: To determine the sensitivity and specificity of dual-energy CT (DECT) virtual noncalcium images (VNCa) with bone and soft tissue reconstructions in the diagnosis of osteomyelitis. Materials & Methods: Between December 1, 2014 to December 1, 2020, 91 patients who had 99 DECT performed for a clinical indication of osteomyelitis with corresponding MRI, triphasic bone scan and/or white blood cell scintigraphy with CT/SPECT performed either 2 weeks before or 1 month after the DECT were retrospectively identified. The presence or absence of osteomyelitis was established using a second imaging test, bone biopsy or surgery. Two radiologists interpreted VNCa images alone and with bone and soft tissue reconstructions for osteomyelitis. Fleiss k statistics was used to assess inter-level agreement. Results: Osteomyelitis was present in 26 cases (26.2%), of which 4 cases (4%) had co-existing septic arthritis. DECT was performed at the following sites: ankle/foot (n = 59), calf (n = 12), knee (n = 3), thigh (n = 7), hip (n = 9), pelvis (n = 6), wrist/hand (n = 1), and shoulder (n = 2). Sensitivity with VNCa images alone was 53.8% and 73.1% and specificity was 84.9% and 71.2%. Sensitivity with VNCa images and bone and soft tissue reconstructions was 80.8% and 80.8% and specificity was 80.8% and 72.6%. Interobserver agreement was 76.7% (76 of 99 cases), for VNCa images alone (k = .487), and 66.7% (66 of 99 patients) for bone and soft tissue reconstructions with VNCa images together (k = .390). Conclusion: When VNCa images were combined with bone and soft tissue reconstructions, there is improved sensitivity in the diagnosis of osteomyelitis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".