The Effect of Anterior Uveitis and Previously Undiagnosed Spondyloarthritis: Results from the DUET Cohort
Bibliographic record
Abstract
OBJECTIVE: Anterior uveitis (AU) is an intraocular inflammatory condition closely linked to spondyloarthritis (SpA). Clinical disease variables may often underestimate the true effect of the disease on patient's quality of life. This study examines AU and associated undiagnosed SpA using established quality-of-life tools to inform clinicians of the effect of these diseases. METHODS: The Dublin Uveitis Evaluation Tool (DUET) algorithm was developed and validated in a cohort of consecutive patients with AU who were all screened by a rheumatologist for the presence of SpA. This same cohort completed vision-related [Vision Core Measure 1 (VCM1)] and general health [Medical Outcomes Study Short Form-36 (SF-36)] questionnaires when AU was active and resolved. RESULTS: VCM1 scores improved with AU resolution. VCM1 did not correlate with vision at baseline, but did on resolution of inflammation. Physical SF-36 scores were reduced during AU episodes and improved on resolution remaining below those of population norms. Subanalysis revealed that SpA scores were more affected than the idiopathic AU group. CONCLUSION: AU affects physical aspects of quality of life more than is appreciated by clinical variables, especially in those with pre-existing, undiagnosed SpA. This study is unique in examining the effect of SpA on patients prior to diagnosis. These results highlight the role of the ophthalmologist in identifying patients with SpA who present with AU using the DUET algorithm.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".