Fluorodeoxyglucose Positron Emission Tomography for Giant Cell Arteritis
Bibliographic record
Abstract
Up to 20 years ago, giant cell arteritis (GCA) was regarded as a form of vasculitis that involved almost solely the cranial arteries in elderly people, and when undiagnosed and untreated, could lead to blindness. Involvement of other arteries in GCA, such as the coronary arteries or the ascending aorta, manifested by myocardial infarction or aortic rupture, respectively, was regarded as rather exceptional1. With the use of 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) in patients suspected of having GCA, this concept changed. We now know that more than half of biopsy-proven patients with GCA have clear but asymptomatic inflammation of their aorta; up to 75% of the subclavian arteries are involved in this form of large-vessel vasculitis (LVV)2. FDG uptake patterns on PET of patients with GCA and Takayasu arteritis in fact are almost identical (because the temporal artery itself cannot be visualized on PET owing to the small size of the artery, its superficial location, and the vicinity of the FDG-consuming brain). The question now arises whether these are 2 different diseases or manifestations of the same disease attacking people at different ages3. The American College of Rheumatology classification criteria for GCA date from 1990, the pre-PET era, and their main emphasis is on cranial symptoms4. They are much less suited for the large-vessel variant of GCA largely unknown at that time, with fever, weight loss, or … Address correspondence to Prof. Dr. D. Blockmans, Katholieke Universiteit Leuven, General Internal Medicine, Herestraat 49, Leuven B-3000, Belgium. E-mail: daniel.blockmans{at}uzleuven.be
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.013 |
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".