The Open Perimetry Interface: An enabling tool for clinical visual psychophysics
Bibliographic record
Abstract
Perimeters are commercially available instruments for measuring various attributes of the visual field in a clinical setting. They have several advantages over traditional lab-based systems for conducting vision experiments, including built-in gaze tracking and calibration, polished appearance, and attributes to increase participant comfort. Prior to this work, there was no standard to control such instruments, making it difficult and time consuming to use them for novel psychophysical experiments. This paper introduces the Open Perimetry Interface (OPI), a standard set of functions that can be used to control perimeters. Currently the standard is partially implemented in the open-source programming language R on two commercially available instruments: the Octopus 900 (a projection-based bowl perimeter produced by Haag-Streit, Switzerland) and the Heidelberg Edge Perimeter (a CRT-based system produced by Heidelberg Engineering, Germany), allowing these instruments to be used as a platform for psychophysical experimentation.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".