Nonparaneoplastic Autoimmune Retinopathy: Scoping Review and Suggested Reporting Guidelines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it