Diffusion-weighted Imaging in Axial Spondyloarthritis: A Measure of Effusion or Does It Elicit Confusion?
Bibliographic record
Abstract
Clinical magnetic resonance imaging (MRI) is based on the resonance properties of hydrogen nuclei under the influence of fluctuating magnetic fields. The spinning proton’s oscillations are enormously affected by its surrounding environment and so the MRI signal emitted from any tissue is profoundly altered by the proton concentration (very high in fat and water) and the tissue’s chemical and physical composition. For example, hyaline cartilage has very high water content (appearing as bright as water on proton density imaging), but the collagen matrix profoundly affects the T2 properties of the protons, so that on T2-weighted imaging hyaline cartilage is dark. In conventional MRI, the tissues of interest are usually stationary and movement of any kind is most often just a darned nuisance. In recent years, MRI is being used to investigate a whole variety of chemical and physical properties of tissues that was previously inconceivable. One such innovation is to measure the movement of free water within tissue at a microscopic level. Using diffusion-weighted imaging (DWI), it is possible for MRI to measure the speed and/or direction of movement of water molecules. The most common uses for DWI have been (1) in the imaging of space-occupying lesions to distinguish tumors from inflammatory masses; and (2) in the brain to provide additional information in ischemic events to detect cell necrosis (stroke) and clarify how much tissue is necrotic (nonviable)1. Most brain tissue has high water content and is highly vascularized. This differs from normal bone marrow, which has very high fat and very low water content and low perfusion, especially in adult fatty marrow. At the onset of brain ischemia, standard MRI sequences show no abnormality, but DWI very quickly shows decreased diffusion of water molecules, and in … Address correspondence to Dr. R.G. Lambert, Department of Radiology and Diagnostic Imaging, 2A2.41 WMC, University of Alberta, Edmonton, Alberta T6G 2B7, Canada. E-mail: rlambert{at}ualberta.ca
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".