Bibliographic record
Abstract
Ischemic strokes occur in 3-4% of patients with GCA 1 , and the causes are multi-factorial.Inflammation of intra-and extracranial vasculature lead to intimal thickening, luminal irregularities, stenoses, and occlusions, which can cause borderzone hypoperfusion and infarction.In situ thrombosis of inflamed vessels can also result in occlusion and distal embolization.1 It would be ideal to treat both inflammatory and thrombotic mechanisms.High-dose corticosteroids are the mainstay of therapy in GCA, but small observational studies have explored the addition of antithrombotics.Use of low-dose aspirin was associated with lower rates of visual loss and strokes. 2 Another study looked at antithrombotics in patients with GCA and found they could be treated with baseline antiplatelet or anticoagulant agents, without an increased risk of bleeding.3 Several case reports have described mixed success with use of anticoagulation in patients with GCA, 4 but none have documented the presence and resolution of iNOT while on anticoagulation.One study looked at iNOT on CT angiography in patients with acute ischemic events (non-arteritic) and found a non-significant trend to resolution of iNOT with dual or triple compared to single antithrombotic therapy.5 Our patient developed new ischemic infarcts despite high dose corticosteroids.On neuroimaging, there was evidence of luminal irregularities as well as an iNOT.We used IV steroids and a limited course of IV heparin and aspirin to target inflammation and thrombosis.We observed the interval resolution of the iNOT after several days of combination therapy, without evidence of bleed.Due to limited prospective studies on use of antithrombotics in GCA, the risks and benefits of such therapy is unknown.In our patient, the small size of her strokes and the progression of disease despite treatment prompted a more aggressive approach.Well-designed prospective studies are necessary to explore combined therapy for treatment-refractive GCA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".