Bibliographic record
Abstract
PURPOSE OF REVIEW: To review recent advances in the management of acute ocular ischemic events, including: transient monocular vision loss, central and branch retinal artery occlusions, and nonarteritic anterior ischemic optic neuropathy. RECENT FINDINGS: Transient monocular vision loss and acute retinal arterial occlusions require immediate diagnosis and management, with recognition of these events as transient ischemic attack or stroke equivalents, respectively. Patients should undergo an immediate stroke workup in a stroke center, similar to patients with acute cerebral ischemia. The treatment of central retinal artery occlusions remains limited despite the growing use of thrombolytic treatments. The indication for these treatments remains under debate. No quality evidence exists to support any therapy, including corticosteroids, in the treatment of nonarteritic anterior ischemic optic neuropathy. The highest priority in management is to rule-out giant cell arteritis. SUMMARY: Effective therapies for the treatment of ischemic events of the retina and optic nerve remain elusive. Clinicians should focus on the prompt recognition of these events as ocular emergencies and immediately refer patients with vascular transient visual loss and acute central and branch retinal arterial occlusions to the nearest stroke center.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".