Bibliographic record
Abstract
Almost two-thirds of patients with giant cell arteritis (GCA) develop ocular symptoms and up to 30% suffer permanent visual loss. We review the three most common mechanisms for visual loss in GCA, describing the relevant ophthalmic arterial anatomy and emphasising how ophthalmoscopy holds the key to a rapid diagnosis. The short posterior ciliary arteries supply the optic nerve head, while the central retinal artery and its branches supply the inner retina. GCA has a predilection to affect branches of posterior ciliary arteries. The most common mechanism of visual loss in GCA is anterior arteritic optic neuropathy due to vasculitic involvement of short posterior ciliary arteries. The second most common cause of visual loss in GCA is central retinal artery occlusion. When a patient aged over 50 years has both anterior ischaemic optic neuropathy and a central retinal artery occlusion, the diagnosis is GCA until proven otherwise, and they should start treatment without delay. The least common culprit is posterior ischaemic optic neuropathy, resulting from vasculitic involvement of the ophthalmic artery and its pial branches. Here, the ophthalmoscopy is normal acutely, but MR imaging of the orbits usually shows restricted diffusion in the optic nerve.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".