Segmentation errors in macular ganglion cell analysis as determined by optical coherence tomography in eyes with macular pathology
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: To evaluate artifacts in macular ganglion cell inner plexiform layer (GCIPL) thickness measurement in eyes with retinal pathology using spectral-domain optical coherence tomography (SD OCT). METHODS: Retrospective analysis of color-coded maps, infrared images and 128 horizontal B-scans (acquired in the macular ganglion cell inner plexiform layer scans), using the Cirrus HD-OCT (Carl Zeiss Meditec, Dublin, CA). The study population included 105 eyes with various macular conditions compared to 30 eyes of 30 age-matched healthy volunteers. The overall frequency of image artifacts and the relative frequency of artifacts were stratified by macular disease. RESULTS: Scan errors and artifacts were found in 55.1% of the 13,440 B-scans in eyes with macular pathology and 26.8% of the 3840 scans in normal eyes. Segmentation errors were the most common scan error in both groups, with more common involvement of both segmentation borders in diseased eyes and anterior segmentation border in normal eyes. CONCLUSION: Segmentation errors and artifacts in SD OCT GCA are common in conditions involving the macula. These findings should be considered when assessing macular GCIPL thickness and careful assessment of scans is suggested.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it