Colour versus grey-scale display of images on high-resolution spectral OCT
Bibliographic record
Abstract
AIM: To determine whether colour or grey-scale images from high-resolution spectral optical coherence tomography (OCT) are superior in visualising clinically important details of retinal structures. METHODS: Patients with macular pathologies were imaged using spectral OCT (OTI, Toronto, Canada). Two reviewers independently analysed the retinal structures and pathologies and graded them on a four-point scale on the basis of the visibility. A third reviewer masked to the results then reviewed images where there was a different score for colour versus grey scale. RESULTS: Statistical analysis showed the grey-scale image to be significantly better in visualising the details of epiretinal membrane, photoreceptor and retinal pigment epithelium layer morphology than the colour scale image (p = 0.00088-0.0006). In 16.17% of eyes, the colour image led to the false impression of photoreceptor disruption. CONCLUSION: Grey-scale images are qualitatively superior to the colour-scale images on high-resolution spectral OCT. Colour images can be misleading, as the displayed colours are false colours, and the observer may see a dramatic change in colour and interpret that as a large change in the OCT reflectivity.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".