Tele-Oncology: A Validation Study of Choroidal and Iris Nevi
Bibliographic record
Abstract
<b><i>Background/Aims:</i></b> To evaluate teleophthalmological assessment of choroidal and iris nevi (tele-oncology) compared to traditional in-person clinical evaluation for detection of either axial or basal growth. <b><i>Methods:</i></b> This is a validation study. All 97 eyes of 99 patients were evaluated with an in-person ocular oncology visit utilizing standard testing, and subsequently had a tele-oncology evaluation with the standardized tests. The tele-oncology reviewer was blinded to the in-person examination findings. The primary study outcome was detection of nevus growth on tele-oncology compared to in-person clinical examination. <b><i>Results:</i></b> Patients had a mean age of 61 years and the majority had nevi located in the choroid (<i>n</i> = 87; 88%). The most common diagnosis was a low-risk nevus (<i>n</i> = 38; 44%). By tele-oncology assessment, 11 eyes showed growth. Ten of these patients had growth confirmed on in-person clinical examination. Resultantly, tele-oncology assessment of choroidal and iris nevi growth had a sensitivity of 100%, specificity of 99%, positive predictive value (PPV) of 91%, and negative predictive value (NPV) of 100%. <b><i>Conclusions:</i></b> The results of this study suggest that tele-oncology is a safe platform for monitoring choroidal and iris nevi for growth, with excellent sensitivity, specificity, PPV, and NPV.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".