Grader learning effect and reproducibility of Doppler Spectral‐Domain Optical Coherence Tomography derived retinal blood flow measurements
Bibliographic record
Abstract
PURPOSE: To investigate grader learning effect and to quantify intergrader reproducibility of Doppler Spectral-Domain Optical Coherence Tomography (SD-OCT) derived retinal blood flow measurements. METHODS: Fifteen healthy young subjects (mean age 28.44; SD 3 years) underwent Doppler SD-OCT scans of one eye using the circumpapillary double circular scan protocol of the Optovue RTVue by one of two experienced operators. One trained (i.e. having undergone certification) and one novice (i.e. preliminary training comprising five standard practice data sets) individual then graded a standardized set of scans, consisting of 15 data sets (session 1) using custom Doppler Optical Coherence Tomography of Retinal Circulation (DOCTORC) software. One week later (session 2), the novice grader underwent further training by grading an additional 15 practice data sets and then both graders subsequently regraded the original 15 data sets. RESULTS: Measurements achieved by a novice grader during session 1 showed a trend to be higher in terms of total retinal venous blood flow (TRBF) and also to be significantly (p = 0.03) higher for venous area, compared with a trained grader. Session 2 results were not significantly different for either grader. The mean TRBF for session 2 for the trained and novice grader was 45.29 ± 9.28 μl/min and 44.39 ± 7.36 μl/min, respectively. The coefficient of repeatability (COR) of session 2 TRBF values between the trained and novice grader was 8.09 μl/min. CONCLUSIONS: There is a grader learning effect which impacts the venous area measurements. Reproducible and repeatable retinal blood flow measurements were achieved among trained graders using DOCTORC software.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".