Functional vision disorder: a review of diagnosis, management and costs
Bibliographic record
Abstract
Functional vision disorder (FVD) is a relatively common diagnosis in ophthalmic practice which can be difficult to make because of clinician's apprehension to miss organic pathology. We review the diagnostic approach to patients with FVD, organic mimics of FVD, its diagnostic and management strategies and associated cost burden. Patients with FVD typically present with visual acuity and/or field loss. Diagnostic work-up should include patient observation, detailed history, pupillary examination, dilated ophthalmoscopy, visual field testing and ganglion cell analysis of the macular complex. Most common organic mimickers of FVD are amblyopia, cortical blindness, retrobulbar optic neuritis, cone dystrophy and chiasmal tumours; however, all could be ruled out by structured diagnostic approach. For patients with unilateral visual loss, bottom-up refraction, fogging of the well-seeing eye in the phoropter, convex lens and base-down prism tests could aid in diagnosis. For patients claiming binocular vision loss, checking for eye movement during the mirror test or nystagmus elicited by an optokinetic drum can be helpful. Effective management of FVD involves reassurance, stress reduction and, if agreed on, management of comorbid anxiety and/or depression. The social cost of FVD is predominately economic as patients typically meet several healthcare providers over multiple visits and often undergo several neuroimaging studies before neuro-ophthalmology referral. Further, inappropriate granting of disability benefits confers additional stigma to patients with organic vision loss.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".