Quantitative Magnetic Resonance Imaging Has Potential for Assessment of Spondyloarthritis: Arguments for its Study and Use
Bibliographic record
Abstract
We read with interest the recent paper on diffusion-weighted imaging (DWI) as a means of supporting the diagnosis of ankylosing spondylitis1, and the corresponding editorial questioning the role of DWI2. The paper1 adds to the growing body of evidence supporting the use of DWI to characterize and quantify inflammation in patients with spondyloarthritis (SpA)3,4. The corresponding editorial argues that DWI is of limited clinical use, and the authors’ main criticisms are summarized as follows: 1. Any sequence can be measured numerically, and this is a facility not confined to DWI. 2. The number of steps required for normalizing measurements and choosing regions of interest (ROI) depends on review of the initial qualitative images and is subjective. 3. The variable diffusion of background marrow poses further challenges for DWI. However, if we look in greater detail at the existing literature on quantitative imaging, it is apparent that many of these issues can be addressed, and indeed there are multiple advantages that quantitative techniques … Address correspondence to Prof. M.A. Hall-Craggs, 2nd Floor, Charles Bell House, 43-45 Foley St., London W1W 7TS, UK. E-mail: margaret.hall-craggs{at}nhs.net
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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 | 0.035 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.036 | 0.056 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".