Using the 24-2 Sita Fast Humphrey to Detect Visual Field Defects Noted in Patients with Neurological Lesions Impacting the Visual Field NormallyAssessed by Octopus Visual Field Testing
Bibliographic record
Abstract
Objective: To investigate the validity of the 24-2 SITA fast Humphrey Visual Field (HVF) testing compared to the established parameters of Octopus Visual Field (OVF) for detecting and monitoring patients with neurologic pathology impacting visual fields Design: Retrospective chart review. Participants: 108 adult patients derived from the Eye Institute of Alberta (EIA) database. Methods: Study participants included adults with OVF testing, at the EIA between September 2015 to September 2017. Three blinded reviewers assessed if findings from each OVF would be identifiable on 24-2 SITA fast HVF testing based on established standardized degree of visual field cut-offs. Demographic data and level of agreement were measured using basic descriptive statistics. Results: In total, 211 individual eye OVFs were scored. Based on our established measurements the 24-2 SITA fast HVF would have identified clinically relevant findings on visual field testing in 197 (93.4%) participants. Of the 6.4% not detected, 64% were due to the patient being unable to fixate on a I2e or I4e isopter, with an additional 18% suffering from movement disorders resulting in exam difficulty (i.e. Parkinson’s disease). Conclusion: The 24-2 SITA fast HVF has potential to be an appropriate alternative test to OVF for detecting and monitoring patients with neurologic pathology impacting visual fields. However, patients with severe vision loss or those not able to fixate on isopters I4e and lower would benefit from more robust testing available in OVF formats. Further head to head comparison of the two visual field modalities is warranted in this group of patients
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".