Signal/Noise Analysis to Compare Tests for Measuring Visual Field Loss and Its Progression
Bibliographic record
Abstract
PURPOSE: To describe a methodology for establishing signal-to-noise ratios (SNRs) for different perimetric techniques, and to compare SNRs of frequency-doubling technology (FDT2) perimetry and standard automated perimetry (SAP). METHODS: Fifteen patients with open-angle glaucoma (median MD, -2.6 dB, range +0.2 to -16.1 dB) were tested six times with FDT2 and SAP (SITA Standard program 24-2) within a 4-week period. Signals were estimated from the average superior-inferior difference between the mean deviation (MD) values in five mirror-pair sectors of the Glaucoma Hemifield Test, and noise from the dispersion of these differences over the six repeated tests. SNRs of FDT2 and SAP were compared by mixed-effects modeling. RESULTS: There was moderate correlation between the signals of FDT2 and SAP (r(2) = 0.68, P < 0.001), but no correlation of noise (r(2) = 0.01, P = 0.16). Although both signal as well as noise estimates were higher with FDT2 compared with SAP, 60% to 70% of sector pairs showed higher SNRs with FDT2. The SNRs of FDT2 were between 20% and 40% higher than those of SAP (P = 0.01). There were no meaningful differences between parametric and nonparametric estimates of signal, noise, or SNR. CONCLUSION: The higher SNRs of FDT2 suggest that this technique is at least as efficient as SAP at detecting localized visual field losses. Signal/noise analyses may provide a useful approach for comparing visual field tests independent of their decibel scales and may provide an initial indication of sensitivity to visual field change over time.
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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 | 0.035 | 0.086 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".