Characteristics of 110 Patients With Functional Visual Loss
Bibliographic record
Abstract
PURPOSE: Functional visual loss (FVL) is characterized by complaints of visual impairment without evidence of an organic cause. Physicians are often reluctant to diagnose FVL; thus, little is known about health care utilization in patients with FVL. DESIGN: Retrospective case series. METHODS: A total of 110 patients seen at 2 university-affiliated neuro-ophthalmology practices who were diagnosed with FVL were included. Medical records were evaluated, and data were collected on demographics, clinical presentation, ophthalmologic examination, neuroimaging, ancillary tests, and other health care provider visits and treatments. RESULTS: More than 70% of patients with FVL were women with a mean age of 37 ± 15 years. The presenting complaint in 71.8% (79/110) of participants was decreased vision, which was bilateral in >50% of cases. Close to half (53/110) endorsed at least 1 coexisting psychiatric or neurologic diagnosis. The mean number of different medical specialists seen before neuro-ophthalmic consultation was 3.7 ± 2.6, and the average number of health care visits was 4.6 ± 4.4. Each patient had 2.2 ± 1.8 neuroimaging studies performed. Fifteen percent of patients underwent unnecessary treatments, including receiving steroids, visual therapy, and prisms. CONCLUSIONS: Patients with FVL typically see at least 3 different health care providers across 4 different visits and undergo at least 2 neuroimaging studies before having neuro-ophthalmic consultation. To avoid this undue burden on patients and the health care system, clinicians should refer patients with suspected FVL to a neuro-ophthalmologist to confirm the diagnosis of FVL and appropriately counsel the patient.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".