High versus standard magnetic resonance image resolution of the cervical spine in patients with axial spondyloarthritis
Bibliographic record
Abstract
Background Sagittal magnetic resonance (MR) images are typically obtained with the same spatial resolution along the entire spine, but cervical vertebrae are smaller and may be harder to assess. Purpose To investigate if high-resolution (high-res) short tau inversion recovery (STIR) and T1-weighted turbo spin echo (T1W) MR imaging (MRI) sequences are superior to standard resolution for detecting inflammatory and structural lesions in the cervical spine of patients with axial spondyloarthritis. Material and Methods Images were obtained in 36 patients. Voxel sizes at high/standard resolution were 1.99/4.33 mm 3 (STIR) and 0.89/3.71 mm 3 (T1W). High-resolution and standard-resolution images were scored by two readers according to the Canada-Denmark (CANDEN) MRI spine scoring system. Results Higher bone marrow edema scores were obtained at high resolution versus standard resolution (mean 2.1 vs. 1.2, P = 0.040), whereas fat lesion scores (1.8 vs. 1.5, P = 0.27) and new bone formation scores (3.5 vs. 2.8, P = 0.21) were similar. High-resolution MRI did not classify more patients as positive for bone marrow edema, fat, or new bone formation in the cervical spine compared to standard resolution. Using lateral radiographs as reference standard, sensitivity for detecting anterior corner syndesmophytes with both high-resolution and standard-resolution MRI was low (range 7–22%) and sensitivity for detecting ankylosis was low to moderate (20–55%), while specificity was high (≥96%). Conclusion High-resolution MRI allowed identification of more inflammatory lesions in the cervical spine in patients with axial spondyloarthritis when compared to standard resolution, but it did not classify more patients as positive for bone marrow edema. The slightly increased sensitivity at high-resolution MRI seemed to be too modest to have any real clinical importance.
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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 | 0.000 | 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".