Stability of Repeated Measures of Contrast Sensitivity Across Spatial Frequency
Bibliographic record
Abstract
Purpose: It is now well accepted that contrast sensitivity (CS) is the most comprehensive single measure of human spatial vision and an emerging clinical tool, especially for assessing patients undergoing refractive surgery and those with retinal and neuro-ophthalmic pathology. Despite its scientific and clinical merits, there are few data on the metrics of CS measurement, perhaps most critically, the stability of assessment across time. Here we provide these results for two of the most predominant sine wave based tests of spatial CS. Methods: Using a standard staircase procedure, right eyes from 15 healthy emmetropic adults (18- 53 yr.) were tested 5-10 times repeatedly (M = 2.4 days between tests) with both the FACT (Vistech) and Vector Vision tests under standard photopic conditions. Contrast thresholds at each spatial frequency (1.5 to 18 c/deg) were obtained, with both test and SF order counterbalanced across trials. Given the repeated tests, participants were not given feedback at any point during the procedure. Results: Performance across tests was very consistent for both tests, especially at mid spatial frequencies. On average, adults differed by 0.30, 0.13, 0.15, 0.22 and 0 .29 CS octaves (an octave is a halving or doubling of CS) at 1.5, 3.0, 6.0, 12.0 and 18.0 cy/deg, respectively. ANOVA and post hoc analyses revealed that there were differences between spatial frequency (p < 0.01), with relatively greatest stability at 3.0 and 6.0 cy/deg. Conclusions: At least among those with normal vision, multiple measurements of contrast sensitivity yield highly consistent and stable estimates of spatial vision. In addition to the implications for measurements of contrast sensitivity in lab settings, this result is of critical importance for clinicians as it implies that patient changes in CS during the course of assessment and treatment likely reflects true degradations or improvements in the underlying optical and neural substrate. Meeting abstract presented at VSS 2017
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".