Gout in the Axial Skeleton
Bibliographic record
Abstract
OBJECTIVE: Gout typically affects the peripheral joints of the appendicular skeleton and rarely involves the axial joints. The literature on axial gout is limited to case reports and case series. This preliminary study was conducted to identify the frequency and characteristics of axial gout. METHODS: Six hundred thirty medical records with ICD codes 274.0, 274.82, and 274.9 for peripheral gout were reviewed. Ninety-two patients had clinical or crystal-proven gout, of which 64 had prior computed tomography (CT) images of the spine performed for various medical reasons. These CT images were reviewed for features of axial gout, which include vertebral erosions mainly at the discovertebral junction and the facet joints, deposits of tophi, and erosions in the vertebral body, epidural space, ligamentum flavum and pars interarticularis. RESULTS: Nine of the 64 patients had radiographic changes suggestive of axial gout. Lumbar vertebrae were most commonly involved, with facet joint erosions being the most common finding. Isolated involvement of the sacroiliac joints was seen in 2 patients. Axial gout had been diagnosed clinically in only one patient. CONCLUSION: Radiologic changes of axial gout were more common than recognized clinically, with a frequency of 14%. Since not all patients had CT images, it is possible that the frequency of axial involvement was even greater. A prospective study is needed to further define this process.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".