Diagnostic values and relevant factors of lumbar posterior lesions in axial spondyloarthritis
Bibliographic record
Abstract
OBJECTIVES: To compare the lumbar posterior lesions between axial spondyloarthritis (axSpA) and lumbar disc herniation (LDH) patients, then their diagnostic value and related factors were evaluated. METHODS: This cross-sectional study included axSpA patients from January 2020 to September 2023. They were classified as ankylosing spondylitis (AS) and non-radiographic axSpA (nr-axSpA) individuals. Canada-Denmark magnetic resonance imaging (MRI) scoring system was used to assess the defects of the lumbar spine. Receiver operating characteristic curve analysis was utilized to determine the value of distinguishing nr-axSpA. Linear regression analyses were adopted to find the relevant factors for lumbar posterior lesions. RESULTS: Ninety-six AS, 98 nr-axSpA, and 108 LDH patients were included. The Canada-Denmark scores were greater in axSpA patients, AS in particular. Furthermore, lumbar posterior lesions can distinguish AS, nr-axSpA, and LDH. Besides, lumbar posterior lesions were positively related to the similar MRI changes in their adjacent structures, but were inversely associated with the other abnormalities. CONCLUSIONS: Lumbar posterior lesions were more serious in axSpA patients. These alterations had value in distinguishing axSpA. Lumbar posterior defects were related to their adjacent components, and they may not fully follow the MRI changing pattern of vertebral bodies and sacroiliac joints.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".