Artifactual Visual Field Defects Identified on Technically “Reliable” Visual Field Studies in a Neuro-Ophthalmology Practice
Bibliographic record
Abstract
Pablo Galarza,1,* Elhanan Parnasa,2,* Noah Guttmann,3 Joshua M Kruger1 1Neuro-Ophthalmology Service, Department of Ophthalmology, Hadassah Medical Center, Jerusalem, Israel; 2Hebrew University-Hadassah School of Medicine, Jerusalem, Israel; 3Independent Researcher, Toronto, Ontario, Canada*These authors contributed equally to this workCorrespondence: Joshua M KrugerNeuro-Ophthalmology Service, Department of Ophthalmology, Hadassah Medical Center, Jerusalem, IsraelTel +972508946493Email kruger@hadassah.org.ilPurpose: To assess the reliability of automated visual field studies with neurological abnormalities and normal reliability indices that were inconsistent with the remainder of the neuro-ophthalmic assessment.Methods: Retrospective observational study from the clinical practice of a neuro-ophthalmologist at a tertiary referral center.Results: From 2230 patient charts, ten cases were identified that met the inclusion criteria. In eight of the cases repeat visual field testing had no reproducible abnormality. Four of these cases were concerning for a bitemporal or homonymous hemianopia. None of the patients, including the two cases with a reproducible defect, developed any convincing manifestations of an organic disease related to the visual field defect.Conclusion: Our findings suggest that even marked neurological abnormalities on reliable automated visual field tests can be false. When the remainder of the neuro-ophthalmic evaluation is inconsistent with the test result, we recommend that clinicians attempt to immediately repeat the visual field study.Keywords: automated perimetry, visual field, Humphrey, reliability
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".