Frequency of seeing characteristics of the short wavelength sensitive visual pathway in clinically normal subjects and diabetic patients with focal sensitivity loss
Bibliographic record
Abstract
AIMS: To define the frequency of seeing (FOS) characteristics of the short wavelength (SW) sensitive visual pathway in clinically normal subjects and in diabetic patients with focal SW sensitivity loss. METHODS: For clinically normal subjects, FOS was assessed at two retinal locations (4.24 degrees and 9.90 degrees eccentricity) for both white on white (WW) and SW stimulus parameters. Inter-examination variability was quantified for the clinically normal subjects only. For patients with diabetes, FOS was assessed inside an area of focal SW sensitivity loss, and at the same eccentricity in the quadrant diametrically opposite, using SW stimulus parameters only. RESULTS: For clinically normal subjects, the group mean SW FOS slope was significantly flatter (p<0.0001) than that of WW at both locations. The coefficient of repeatability for SW FOS slope was+/-41.55 dB(-1) (relative to a group mean sensitivity of 23.98 dB(-1)) and+/-19.98 dB(-1) (group mean sensitivity 16.15 dB(-1)) for 4.24 degrees and 9.90 degrees , respectively. For the patients with diabetes, the group mean SW FOS slope was significantly flatter (p=0.020), and group mean SW threshold significantly higher (p=0.007) in the area of focal SW sensitivity loss than in that of the non-focal sensitivity loss. CONCLUSIONS: The results of this study suggest that the clinical utility of SW automated perimetry will be limited by a greater magnitude of measurement variability, as indicated by a flatter FOS slope, compared to conventional automated perimetry.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".