Eccentricity and measurement variability and repeatability with the retinal thickness analyser
Bibliographic record
Abstract
AIMS: To define the variability and repeatability of retinal thickness measurements using the retinal thickness analyser (RTA) and to elucidate any interaction between eccentricity (that is, position relative to the fovea) and variability and repeatability. METHODS: The sample comprised 20 normal subjects of mean age 33 years. Each subject attended for two visits. Repeated RTA scans were acquired centred on the fovea and for any one of the four possible non-foveal scan areas. The mean retinal thickness (+SD) was calculated for a series of concentric circular bands centred on fixation. A repeated measures analysis of variance (ANOVA) was used to determine any significant interaction between the variability of RTA thickness values and eccentricity. RESULTS: The group mean coefficient of variation and coefficient of repeatability were highest at the fovea. The repeated measures ANOVA revealed that the within test variability of RTA measurements varied significantly with eccentricity (p<0.0001). Similarly, the between test repeatability varied significantly with eccentricity (p = 0.045). CONCLUSION: The significantly elevated within test variability and between test repeatability in the foveal area need to be considered when using the RTA to evaluate patients with macular disease.
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
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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".