Reproducibility of Retinal and Optic Nerve Head Perfusion Measurements Using Scanning Laser Doppler Flowmetry
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: To evaluate the reproducibility of full-field perfusion analysis using scanning laser Doppler flowmetry (SLDF) for perfusion measurements of the neuroretinal rim of the optic nerve head and the peripapillary retina in patients with open-angle glaucoma or ocular hypertension, and in normal subjects. PATIENTS AND METHODS: SLDF perfusion measurements of the neuroretinal rim and the peripapillary retina were performed on 20 patients with open-angle glaucoma or ocular hypertension (group G) and 20 normal volunteers (group N). Each subject underwent two independent sessions, 30 minutes apart, each involving 5 high quality images. Intrasession and intersession reproducibility coefficients for flow, volume, and velocity were calculated for a single image and for means of 3 and 5 images using analysis of variance models. flow, the intrasession coefficient of reliability was 0.99 each for the rim, nasal retina, and temporal retina in group G and 0.93, 0.93, and 0.95, respectively, in group N. The intersession coefficient of reliability for flow was 0.99 for the rim, 0.95 for the nasal retina, and 0.87 for the temporal retina in group G and 0.87, 0.82, and 0.80, respectively, in group N. Compared with single image analysis, intrasession and intersession reproducibility were generally better when a mean of 3 images and substantially better when a mean of 5 images was used. CONCLUSION: SLDF full-field perfusion analysis is markedly more reproducible than the original software using 10 x 10 pixel windows. Obtaining mean values for at least 3 images improves the intrasession and intersession reproducibility of this technique.
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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 | 0.004 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".