Effects of Blur and Repeated Testing on Sensitivity Estimates with Frequency Doubling Perimetry
Bibliographic record
Abstract
PURPOSE: To investigate the effect of blur and repeated testing on sensitivity with frequency doubling technology (FDT) perimetry. METHODS: One eye of 12 patients with glaucoma (mean deviation [MD] mean, -2.5 dB, range +0.5 to -4.3 dB) and 11 normal control subjects underwent six consecutive tests with the FDT N30 threshold program in each of two sessions. In session 1, blur was induced by trial lenses (-6.00, -3.00, 0.00, +3.00, and +6.00 D, in random order). In session 2, only the effects of repeated testing were evaluated. The MD and pattern standard deviation (PSD) indices were evaluated as functions of blur and of test order. By correcting the data of session 1 for the reduction of sensitivity with repeated testing (session 2), the effect of blur on FDT sensitivities was established, and its clinical consequences evaluated on total- and pattern-deviation probability maps. RESULTS: FDT sensitivities decreased with blur (by <0.5 dB/D) and with repeated testing (by approximately 2 dB between the first and sixth tests). Blur and repeated testing independently led to larger numbers of locations with significant total and pattern deviation. Sensitivity reductions were similar in normal control subjects and patients with glaucoma, at central and peripheral test locations and at locations with high and low sensitivities. However, patients with glaucoma showed larger deterioration in the total-deviation-probability maps. CONCLUSIONS: To optimize the performance of the device, refractive errors should be corrected and immediate retesting avoided. Further research is needed to establish the cause of sensitivity loss with repeated FDT testing.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".