Bibliographic record
Abstract
Automated visual field testing, or perimetry, is a non-invasive method to assess differential light sensitivity across the retina. Traditionally, perimetry has been confined to clinical settings which are highly controlled. However, recent advancements in portable perimetry have expanded the environments over which testing can occur. This necessitates the development of faster and more robust algorithms to accommodate these diverse conditions. This thesis presents three approaches to address these needs. The first is the improvement of a reconstruction approach which allows for the testing of fewer locations within the visual field. The addition of principal component analysis generalizes the reconstruction model better under cases where data is limited. The second approach is a modification of the standard Bayesian perimetric algorithm to increase the speed of testing while improving specificity and sensitivity, making it suitable for the screening of glaucoma. Finally, the third approach is a complete rethinking of how perimetry testing occurs. The usual approach for testing correlated visual field locations involves assessing each location separately, determining fully the threshold before moving to the next location. In this new approach, a trial-based method is proposed whereby a single trial at one location can be used to update the threshold at all other locations. This new method, which is called TORONTO, can halve the test duration when compared to other traditional and state-of-the-art algorithms while achieving similar or lower estimation errors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".