The Impact of Artificial Light Scatter on Scanning Laser Tomography
Bibliographic record
Abstract
PURPOSE: The impact of cataract (which frequently occurs alongside glaucoma) on scanning laser tomography (SLT) is poorly understood. The aim of this pilot study was to determine the impact of artificial light scatter on SLT estimates of optic nerve head (ONH) topography. METHODS: The sample comprised 10 healthy, young subjects of mean age 23.5 years. One eye of each subject was randomly selected. Cells filled with increasing concentrations of 0.50-microm diameter polystyrene microspheres were prepared. The cells were mounted in front of the objective lens of the Heidelberg Retina Tomograph (HRT) II and were tilted at an angle of 20 degrees to eradicate any surface reflections. Three sets of ONH scans were initially acquired without any light scatter cell in place and then three further sets were acquired for each of four different concentrations of microspheres in a randomized order. The impact of artificial light scatter on cup-to-disc area ratio, cup volume, rim volume, cup shape measure, height variation contour, and mean retinal nerve fiber layer (RNFL) thickness was evaluated. RESULTS: Repeated-measures analysis of variance showed that there was no significant change in cup-to-disc area ratio, cup volume, rim volume, height variation contour, cup shape measure, or mean RNFL thickness as a function of increasing light scatter cell concentration. CONCLUSION: Artificial light scatter had no statistically significant impact on the stereometric parameters of the HRT II. From a clinical perspective, useful SLT data can be acquired with confidence from patients with diagnosed/suspected glaucoma and concomitant cataract.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".