Variability and repeatability of quantitative, SD-OCT doppler blood flow in young and elderly healthy subjects
Bibliographic record
Abstract
Purpose: The purpose was to determine the within-session variability and between-session repeatability of SD-OCT Doppler measurement of retinal blood flow in young and elderly healthy subjects. \nMethods: SD-OCT Doppler blood flow was non-invasively measured using the RTVue system (Optovue Inc., USA). One eye of each of 6 healthy young (mean age 25.7; SD 3.5 years) and 6 healthy elderly (mean age 63.1; SD 1.8 years) subjects was randomly selected for the study and the pupil was dilated using Mydriacyl 1%. A double circular scanning \npattern was employed. A minimum of six separate SD-OCT Doppler measurements (i.e. each separate measurement comprising an upper nasal pupil scan and a lower nasal pupil scan) were acquired at each session. Measurements were repeated on a second day. Retinal blood flow was \ncalculated, using data from valid scans only, by summing flow from all detectable venules. The coefficient of variation and the coefficient of repeatability were calculated for each individual. \nResults: The individual COVs for retinal blood flow for young subjects ranged from 2 to 51.9% (median 28.8%) and for the elderly subjects ranged from 0.6 to 81.2% (median 9.9%). The group mean CORs for retinal blood flow for young subjects were 30.5 μl/min (median 29.5 μl/min, relative to a mean effect 39.8 μl/min) and for elderly subjects were \n16.9 μl/min (median 7.2 μl/min, relative to a mean effect 34.7 μl/min). \nConclusions: The Doppler SD-OCT in general gave consistent \nmeasurements of retinal blood flow in normal subjects, while the data was far more repeatable for the elderly group. The relatively small sample size needs to be considered when interpreting these results. \n
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".