Bibliographic record
Abstract
Takayasu arteritis (TA) is a systemic granulomatous vasculitis of large vessels with low incidence and nonspecific symptoms, and late diagnosis and management lead to complications such as stroke, acute myocardial infarction or peripheral ischemia. This case illustrates the complexity of TA diagnosis because its symptomatology is frequently mistaken as chronic migraine. Therefore, without a syndromic approach, it is more likely to increase comorbidity and progression of the disease. This paper deacribes a 25-year-old woman with chronic migraine for 8 years, with recurrent admissions to emergency service, and specialist outpatient consultations. The patient has a history of fugax amaurosis, hypertension and claudicating chest pain that required treatment with corticoid, hydroxychloroquine, methotrexate and neflunomide, due to a stenosis of 50-70% of the left carotid artery and thickening of the walls of the distal thoracic aorta and the proximal abdominal aorta, corresponding to TA, with improvement of its subsequent symptomatology to treatment for rheumatology. J Med Cases. 2019;10(1):18-20 doi: https://doi.org/10.14740/jmc3238
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".