Bibliographic record
Abstract
Abstract Paraneoplastic occular disorders are clinical syndromes resulting from nonmetastatic systemic effects of underlying cancers. While not fully elucidated the underlying mechanism is presumed to be a cross reaction of the immune reaction to the underlying cancer that attaches normal host tissue. This cross reaction can happen in a variety of occular tissues resulting in dysfunction of the retina, optic nerve and tear film and extraocular muscles. (1‐3). The lists of cancers and associated antibodies is constantly growing but, in many cases, the clinical phenotype is very similar. Cancer associated retinopathy (CAR) for example is associated with several antibodies including anti‐recoverin, anti‐alpha enolase, anti‐transducin, and anti‐carbonic anhydrase(2, 3). Each of these antibodies have been associated with a distinct cancer including skin, genital and small cell lung cancer (SCLC). CAR causes a slowly progressive asymmetric vision loss due to damage to retinal ganglion cells and bipolar cells which can result in optic nerve pallor. Melanoma associated retinopathy (MAR), similarly can present with nyctalopia, photopsia’s and loss of peripheral vision. The antibodies associated with MAR; anti‐transducin, anti‐rhodopsin and anti‐arrestin typically attack the on bipolar system. As with CAR the visual dysfunction can predate the discovery of the cancer. Conversely, autoimmune related retinopathy and optic neuropathy occurs without an underlying cancer and is associated with anti‐GAD and anti‐ anti‐collapsin‐responsive mediator protein‐5 (CRMP‐5) antibodies(2). In all three cases electroretinograms are key to making the diagnosis as the objective clinical examination can be normal. Electroretinograms are key to distinguishing retinal dysfunction which can cause an optic neuropathy from a paraneoplastic optic neuropathy (PON). The classical antibody in PON is. CRMP‐5 is associated with SCLC and thymoma and can produce optic disc edema with subsequent atrophy. The treatment and management of occular paraneoplastic disorders requires appropriate investigations to look for an underlying cancer. Knowing the specific antibody associated with the disorder can be helpful in this regard and in some cases a panel of antibody tests is appropriate in the right clinical context(4). Treatment of the underlying cancer can, in some cases, can lead to a resolution of the clinical symptoms. Symptom control can also be accomplished using immunosuppression with steroids or IVIG and in some cases plasmapheresis has been useful(4, 5). References Grewal DS, Fishman GA, Jampol LM. Autoimmune retinopathy and antiretinal antibodies: a review. Retina. 2014;34(5):827–45. Bataller L, Dalmau J. Neuro‐ophthalmology and paraneoplastic syndromes. Current opinion in neurology. 2004;17(1):3–8. Rahimy E, Sarraf D. Paraneoplastic and non‐paraneoplastic retinopathy and optic neuropathy: evaluation and management. Survey of ophthalmology. 2013;58(5):430–58. Pelosof LC, Gerber DE. Paraneoplastic syndromes: an approach to diagnosis and treatment. Mayo Clin Proc. 2010;85(9):838–54. Damek DM. Paraneoplastic Retinopathy/Optic Neuropathy. Current treatment options in neurology. 2005;7(1):57–67.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".