Effects of Blur and Repeated Testing on Sensitivity Estimates with Frequency Doubling Perimetry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| 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 it