Effect of Eccentric and Inconsistent Fixation on Retinal Optical Coherence Tomography Measures
Bibliographic record
Abstract
OBJECTIVE: To assess the relative stabilities of optical coherence tomography (OCT)-based retinal volume and central foveal thickness measurements in the setting of eccentric or inconsistent fixation. METHODS: Ten healthy right eyes underwent multiple macular OCT centered at fixation. To model the effect of eccentric or inconsistent fixation, OCT was repeated with scan centers precisely shifted by 0.50, 1.00, and 1.50 mm in each of 4 directions. At each scan location, retinal volumes within a series of radii of the scan center, as well as central foveal thickness, were calculated. The main outcome measure was the percentage effect of decentered scanning on each OCT-based variable. RESULTS: Central foveal thickness was the variable most affected in this model of eccentric and inconsistent fixation. This variable demonstrated changes from baseline-centered scans of up to 69.4%. Retinal volumes within a radii of the scan center measuring 1.11 mm or greater were least affected by decentered scanning, demonstrating maximum changes from baseline-centered scans of only 15.7% (P<.001 vs foveal thickness). CONCLUSION: Optical coherence tomography-based retinal volume quantification provides a more stable measure than foveal thickness in the setting of eccentric or inconsistent fixation as may occur in the setting of macular pathologic conditions.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".