Assessment of indeterminate melanocytic choroidal tumours with optical coherence tomography: A cohort study
Bibliographic record
Abstract
Background: It is difficult to differentiate large choroidal naevi from small melanomas. The management of patients with such ‘indeterminate melanocytic tumours’ is controversial. This is because over-treatment of naevi can cause unnecessary visual loss whereas delayed treatment of melanoma may have fatal consequences. Several studies have shown that serous retinal detachment overlying an indeterminate melanocytic choroidal tumour predicts growth of these tumours; however, these studies have mostly been based on ophthalmoscopy. Optical coherence tomography (OCT) facilitates the detection of subtle retinal detachment. It is not known, however, whether minimal retinal detachment is clinically relevant. The aim of our study was to evaluate OCT as a tool for predicting growth of indeterminate melanocytic choroidal tumours. Methods: Forty-five patients with a recently-detected, indeterminate melanocytic choroidal tumour were examined with OCT and the findings were correlated with subsequent tumour growth. Results: After a mean follow-up of 15 months, 9 of 17 tumours with SRF showed growth as compared to 1 out of 28 tumours without SRF. Tumours with SRF increased in thickness by an average of +0.26mm [95% confidence interval (CI): -0.06 to +0.57] as compared to a mean decrease of -0.12mm [95% CI : -0.22 to -0.03] in tumours without SRF. Of the eight tumours requiring treatment because of observed growth, seven showed overlying SRF as compared to none of the tumours without SRF. Conclusions: OCT is useful in predicting growth of indeterminate melanocytic choroidal tumours.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".