Diagnostic Accuracy of Glaucoma With Sector-Based and a New Total Profile–Based Analysis of Neuroretinal Rim and Retinal Nerve Fiber Layer Thickness
Bibliographic record
Abstract
PURPOSE: To compare the diagnostic accuracy of conventional sector-based analysis with a method devised to detect the smallest localized neuroretinal rim and retinal nerve fiber layer thickness (RNFLT) damage. METHODS: One eye of 151 glaucoma patients and 83 healthy controls (median age and MD, 71.7 and 66.7 years, and -3.6 and -0.3 dB, respectively) was imaged with spectral-domain optical coherence tomography (OCT). Bruch's membrane opening-minimum rim width (BMO-MRW) and RNFLT were determined at 1° intervals and also averaged for each sector. A classification of glaucoma was made with sectoral analysis when the sectoral value was below the 1%, 5%, or 10% normative limit (from an independent normative dataset); and with total analysis when a given number of measurements was below the 1%, 5%, or 10% normative limit. RESULTS: With the 1% normative limit, BMO-MRW sectoral analysis yielded sensitivity of 87% and specificity of 92%; while at the same specificity (92%), total analysis yielded sensitivity of 88%. With RNFLT, sectoral analysis yielded sensitivity of 85% and specificity of 95%; while at the same specificity (95%), total analysis yielded sensitivity of 83%. The results for the 5% and 10% normative limits yielded lower specificity but higher sensitivity. In the whole glaucoma population, none of the sensitivity values of the sectoral and total analysis at the same specificities were statistically different. CONCLUSIONS: The diagnostic accuracy of sectoral analysis was equivalent to total analysis. These results indicate that BMO-MRW and RNFLT defects were wide and deep enough for detection by conventional sectoral analysis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.010 |
| 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".