Nonparaneoplastic Autoimmune Retinopathy: Scoping Review and Suggested Reporting Guidelines
Bibliographic record
Abstract
PURPOSE: To investigate trends in the diagnostic approach to nonparaneoplastic autoimmune retinopathy (npAIR). METHODS: We queried PubMed for clinical reports on npAIR published between January 2016 and September 2025. Articles were assessed to determine criteria used to establish diagnosis of npAIR using a standardized grading system. Articles were categorized as case reports (≤3 patients) or case series (>3 patients). RESULTS: 36 case reports and 41 case series met eligibility criteria (755 total cases). Author subspecialty included 34% uveitis, 20% inherited retinal disease (IRD), 16% general retina, 10% miscellaneous, and 19% unknown specialty. Over 80% of publications reported electroretinography and anti-retinal antibody testing for diagnosis of npAIR. Fundus autofluorescence (FAF) was performed in 67% of case reports and at least one patient in 51% of case series. Widefield FAF was used in 19% of case reports and in at least one patient in 20% of case series. Genetic testing was reported in 22% of case reports and in at least one patient in 27% of case series. Studies with an IRD specialist as first or last author most commonly used genetic testing (35%). CONCLUSIONS: Literature on npAIR is hampered by variability in classification schemes and incomplete reporting. Nonspecific electroretinography testing and antiretinal antibody testing are widely employed while widefield autofluorescence testing and genetic testing are not commonly used. Expanded access to these tools provides an opportunity to update diagnostic criteria of npAIR. Improved classification will permit us to better understand the natural history of disease.
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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 | 0.057 | 0.233 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.045 | 0.033 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".