Impact of simulated light scatter on scanning laser Doppler flowmetry
Bibliographic record
Abstract
AIM: To determine the impact of simulated light scatter on scanning laser Doppler flowmetry (SLDF) assessment of retinal capillary blood flow and retinal image quality. METHODS: One eye of 10 normal subjects (mean (SD) age 24 (1.7) years, range 22-27) was randomly selected. Varying concentrations of polystyrene microspheres were suspended in optically clear cells to simulate light scatter. The microsphere concentrations used were 0.05%, 0.03%, 0.02%, 0.01%, and a cell containing only water. LogMAR visual acuity and contrast sensitivity were measured both with and without cells. Optimal focus and alignment was established by acquiring three SLDF images each of the optic nerve head (ONH) and of the macula using the Heidelberg retina flowmeter (HRF) with no cell in place. SLDF images were subsequently acquired with each of the light scatter cells mounted in front of the HRF. The group mean retinal capillary blood flow was compared using repeated measures analysis of variance (reANOVA) as a function of microsphere concentration. RESULTS: Retinal capillary blood flow increased significantly in the ONH, nasal macula, fovea, and temporal macula with increasing microsphere concentration (p<0.0001). Using Dunnett's post hoc test, retinal capillary blood flow was found to be significantly increased relative to the no cell condition for the 0.03% and 0.05% cell concentrations. CONCLUSIONS: Simulated light scatter produces an artifactual increase in retinal capillary blood flow. The impact of cataract on SLDF measurements has yet to be determined.
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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 | 0.001 | 0.006 |
| 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 | 0.000 |
| 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".