Gene Expression Profiling as an Adjunctive Measure to Guide the Management of Indeterminate, High-Risk Choroidal Melanocytic Lesions: A Pilot Study
Bibliographic record
Abstract
Purpose: To describe our early experience with gene expression profiling (GEP) assessment for juxtafoveal, subfoveal, and peripapillary indeterminate high-risk melanocytic lesions to assist in making early treatment decisions in patients who did not feel comfortable with either close observation or definitive treatment. Methods: A prospective cohort of patients with indeterminate lesions who underwent GEP were enrolled. Nonparametric statistical analysis was utilized given the small sample size. Results: Fifteen patients were included in this series. Six (40%) were class 1A and 9 (60%) class 1B. Class 1A and 1B lesions had a median of three and four clinical risk factors, respectively (p = 0.27). There was no statistically significant difference for the largest basal diameter between the classes (p = 0.31); however, class 1B lesions were thicker than class 1A lesions (p = 0.03). None of the class 1A lesions showed definite growth or metastasis over a mean follow-up period of 17.1 ± 1.8 months from fine needle aspiration biopsy. All class 1B patients opted for plaque brachytherapy, and to date none of these patients have developed metastasis, with a mean follow-up of 18.7 ± 8.4 months. Conclusion: There may be a role for GEP assessment in high-risk, indeterminate, posteriorly located choroidal lesions to assist in treatment planning.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".