Outcome of Takayasu Arteritis with Inactive Disease at Diagnosis: The Extent of Vascular Involvement As a Predictor of Activation
Bibliographic record
Abstract
OBJECTIVE: Some patients with Takayasu arteritis (TA) have inactive disease at the time of diagnosis. The objective of our study was to investigate the clinical outcomes and factors that predict disease activation in patients with clinically inactive TA. METHODS: The medical records of patients diagnosed with TA between 1990 and 2012 were reviewed. At the time of diagnosis, patients were identified as having inactive disease according to the National Institutes of Health definition. Patients who went on to develop active disease during followup were classified as the "activation group". The pattern of vascular involvement was classified according to the International Conference on TA, 1994. RESULTS: A total of 59 patients with TA were classified as having inactive disease at the time of diagnosis. During the followup, 13 (22.0%) of these experienced TA activation (median followup, 37.0 mos; activation group). The remaining 46 (78.0%) did not experience disease activation (stable group). Renovascular hypertension was more common in the activation group than in the stable group (5/13, 38.5% vs 4/46, 8.7%, p = 0.019). Further, type V, which is the most extensive, was more common in the activation group (12/13, 92.3%) than in the stable group (18/46, 39.1%, p = 0.008). Multivariate analysis identified type V disease (OR 10.969, 95% CI 1.144-105.182, p = 0.038) as being significantly associated with an increased risk of disease activation. CONCLUSION: Substantial portions of patients with clinically inactive TA at the time of diagnosis experienced disease activation during followup. Type V disease may be an important predictive factor for disease activation in patients with clinically inactive TA.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".