SENSITIVITY AND SPECIFICITY OF THE OPTOS OPTOMAP FOR DETECTING PERIPHERAL RETINAL LESIONS
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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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".