Imaging and Perimetry Society Standards and Guidelines
Bibliographic record
Abstract
PURPOSE: To provide readers with standards, recommendations, guidelines, and requirements for the application of perimetry to clinical ophthalmic practice and scientific study. METHODS: A working group of perimetry and visual field specialists from many parts of the world constructed a document that would allow current and future perimeters to be assessed by the same criteria. Because hardware and software technology, statistical procedures and clinical conditions are constantly changing, the characteristics in this paper emphasize general concepts rather than specific implementations employed by current devices. RESULTS: Critical aspects of perimetry included indications for perimetry, perimetric techniques, stimulus characteristics, test administration, patient preparation, data display, statistical analysis, interpretation of visual field findings, a glossary of terms and definitions, and standards for comparison of different perimetric tests. Each of these topics is discussed, along with their advantages and disadvantages. CONCLUSIONS: These guidelines serve as a basis for practitioners to evaluate their perimetric needs in relation to their clinical practice and patient population so that informed decisions can be made for visual field testing. In addition, these issues should be used as a cornerstone for future technological and practical improvements to the visual field diagnostic procedures.
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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 | 0.021 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.015 |
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".