Zygapophyseal Joint Fusion in Ankylosing Spondylitis Assessed by Computed Tomography: Associations with Syndesmophytes and Spinal Motion
Bibliographic record
Abstract
OBJECTIVE: Because zygapophyseal joints (ZJ) are difficult to visualize on radiographs, little is known about the relationship of ZJ fusion to other features of spinal damage in ankylosing spondylitis (AS). We used computed tomography (CT) to investigate the concordance of ZJ fusion and syndesmophytes, and examined the contribution of both features to spinal motion. METHODS: We performed thoracolumbar CT scans (T10-T11 to L3-L4) on 55 patients. Two readers scored scans for ZJ fusion, which were compared to syndesmophyte height and extent of bridging, measured by computer algorithm at the same levels. We used multiple regression analysis to evaluate the relative contributions of ZJ fusion and syndesmophytes to spinal mobility. RESULTS: Fifty-one percent of patients had ZJ fusion in at least 1 vertebral level. Fusion was present in 129 of 652 individual ZJ. Syndesmophytes and bridging were often present in vertebral levels without ZJ fusion, suggesting that syndesmophytes most often develop first. ZJ fusion was present in 34% of vertebral levels with syndesmophytes and 55.9% of levels with bridging, suggesting a closer association with bridging. Syndesmophytes and ZJ fusion had similar associations with the modified Schober test, but syndesmophytes were more strongly associated with limitations in lateral thoracolumbar flexion. ZJ rarely showed new fusion over 4 years. CONCLUSION: Thoracolumbar ZJ fusion in AS is rarely present at vertebral levels without syndesmophytes. Syndesmophytes, therefore, likely appear before ZJ fusion at a given vertebral level. Both syndesmophytes and ZJ fusion contribute to limited forward lumbar flexion, but syndesmophytes contribute more to limited lateral flexion.
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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 | 0.004 |
| 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) | 0.001 | 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".