Neuro-ophthalmic manifestations of autoimmune disorders: diagnostic pearls & pitfalls
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this review is to highlight a clinical-anatomical approach to localizing neuro-ophthalmic manifestations of associated autoimmune disorders. RECENT FINDINGS: Our understanding of autoimmune conditions has changed considerably over recent years, particularly with the emergence of novel autoantibodies. Cardinal neuro-ophthalmic signs and symptoms of antibody-mediated autoimmune disorders have been well characterized; knowledge thereof may be the first step towards an accurate diagnosis. SUMMARY: A thorough history, further refined by a comprehensive examination are cornerstones to disease localization in clinical medicine. Taken together, these essential steps both guide investigations and facilitate early recognition of autoimmune disorders. From a neuro-ophthalmic perspective, it is important to understand heralding signs and symptoms of autoimmune syndromes, avoid cognitive errors, and remain mindful of common diagnostic pitfalls to optimize care. VIDEO ABSTRACT: http://links.lww.com/COOP/A61.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".