SENSITIVITY AND SPECIFICITY OF THE OPTOS OPTOMAP FOR DETECTING PERIPHERAL RETINAL LESIONS
Why this work is in the frame
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Bibliographic record
Abstract
In Brief Purpose: To compare the sensitivity and specificity of the Optomap Panoramic200 wide-field confocal scanning laser imaging system for detecting peripheral retinal lesions. Methods: Optomap images were obtained in patients with known retinal pathology. Two masked retinal specialists evaluated Optomap images to identify lesions requiring referral to a retinal specialist. Their performance was compared to gold standard examination with scleral indentation performed by a retinal specialist. Sensitivity was calculated overall and again for lesions that were found on clinical examination to require treatment. These sensitivities were calculated separately for lesions posterior and anterior to the equator. Specificity was calculated from fellow eyes that were found to have no pathology on clinical examination. Results: For retinal lesions posterior to the equator, sensitivity was 74% (95% confidence interval [95% CI] 61%–87%) overall for all lesions and 76% (95% CI 59%–93%) for lesions requiring treatment. For anterior lesions, sensitivity was 45% (95% CI 28%–62%) overall and 36% (95% CI 14%–58%) for treatable lesions. Specificity was 85% (95% CI 63%–100%). Conclusions: The Optomap showed high specificity and moderate sensitivity for lesions posterior to the equator and low sensitivity for lesions anterior to the equator. The sensitivity and specificity of the Optos Optomap Panoramic200 imaging system for the detection of peripheral retinal pathology was evaluated. The Optomap showed high specificity and moderate sensitivity for lesions posterior to the equator and low sensitivity for lesions anterior to the equator.
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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 | 0.000 | 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