Detection of glaucoma: the role of new functional and structural tests
Bibliographic record
Abstract
PURPOSE OF REVIEW: It is thought that our current techniques for investigating open-angle glaucoma may not be the most sensitive ones for the earliest detection of the disease. Newly developed psychophysical and imaging techniques may have an important role in clinical practice. This review outlines some of the issues involved in adopting these techniques. RECENT FINDINGS: To date there are many cross-sectional studies that report on the sensitivity and specificity characteristics of these techniques based on our current definitions of open-angle glaucoma. There are a limited number of studies available that show the efficacy of the new techniques for the early detection of open-angle glaucoma. SUMMARY: More longitudinal studies are now needed to demonstrate that the new techniques that are being adopted in clinical practice can detect glaucoma earlier. More importantly, studies are required to prove that early treatment in individuals detected with these new techniques makes a meaningful impact on the patients' prognosis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".