Impact of simulated light scatter on the quantitative, noninvasive assessment of retinal arteriolar hemodynamics
Bibliographic record
Abstract
We determine the impact of artificial light scatter on quantitative, noninvasive assessment of retinal arteriolar hemodynamics. One eye from each of 10 healthy young subjects between the ages of 18 and 30 (23.6+/-3.4) is randomly selected. To simulate light scatter, cells comprising a plastic collar and two plano lenses are filled with solutions of differing concentration of polystyrene microspheres (Polysciences Inc., USA). We prepare 0.002, 0.004, 0.006, and 0.008% microsphere concentrations as well as distilled water only. The Canon laser blood flowmeter (CLBF) is used to noninvasively assess retinal arteriolar blood flow. After a preliminary screening to confirm subject eligibility, seven arteriolar blood flow measurements are taken by randomly placing the cells between the instrument objective lens and the subjects' cornea. To achieve a baseline, subjects are first imaged with no cell in place. Both low- and high-intensity CLBF laser settings are assessed. Our light scatter model results in an artifactual increase of retinal arteriolar diameter (p<0.0001) and thereby increased retinal blood flow (p<0.0001). The 0.006 and 0.008% microsphere concentrations produce significantly higher diameter and flow values than baseline. Centerline blood velocity, however, is not affected by light scatter. Retinal arteriolar diameter values are significantly less with the high-intensity laser than with the low-intensity laser (p=0.0007). Densitometry assessment of vessel diameter is increasingly impacted as the magnitude of artificial light scatter increases; this effect can be partially negated by increasing laser intensity. A cataract is an inevitable consequence of aging and, therefore, care must be exercised in the interpretation of studies of retinal vessel diameter that use similar densitometry techniques.
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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 | 0.005 |
| 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".