Sensitivity and specificity of spinal inflammatory lesions assessed by whole‐body magnetic resonance imaging in patients with ankylosing spondylitis or recent‐onset inflammatory back pain
Bibliographic record
Abstract
OBJECTIVE: To determine the diagnostic utility of different spinal inflammatory lesions assessed by whole-body magnetic resonance imaging (MRI) in patients with ankylosing spondylitis (AS) or with recent-onset inflammatory back pain (IBP) compared with healthy controls. METHODS: We scanned 35 consecutive patients with AS fulfilling the modified New York criteria, 25 patients with IBP of <24 months' duration (both groups were age < or =45 years and had a Bath Ankylosing Spondylitis Disease Activity Index score > or =4), and 35 healthy age- and sex-matched volunteers using whole-body MRI STIR sequences of the spine. MRIs were independently assessed in random order by 3 readers blinded to patient identity. Inflammatory spinal lesions were recorded consistent with definitions proposed by the Canada/Denmark International MRI Working Group: vertebral corner inflammatory lesions (CIL) and noncorner inflammatory lesions in central sagittal slices and lateral inflammatory lesions (LIL) in lateral slices. Concordantly scored lesions for the 3 possible reader pairs were used in the analysis of sensitivity, specificity, likelihood ratios (LRs), and areas under the curve for the entire spine and by spinal segment. RESULTS: Diagnostic utility was optimal when > or =2 CIL were recorded (for patients with AS, values for sensitivity, specificity, and positive LR were 69%, 94%, and 12, respectively, and for patients with IBP were 32%, 96%, and 8, respectively). LIL had high specificity (97%) but low sensitivity (31%). Nine controls had > or =1 CIL, but only 2 controls had >2 CIL. CONCLUSION: Diagnostic utility of STIR MRI for AS is optimal when > or =2 CIL are present. A single CIL can be found in up to 26% of healthy individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".