Hand-held high-resolution spectral domain optical coherence tomography in retinoblastoma: clinical and morphologic considerations
Bibliographic record
Abstract
PURPOSE: Hand-held spectral domain optical coherence tomography (HHSD OCT) has greatly expanded the imaging/diagnostic capacity for clinicians managing children with intraocular retinoblastoma. We present our early experience with HHSD OCT and conventional spectral domain OCT imaging in these patients. METHODS: In this retrospective cross-sectional observational study, infants were imaged during examination under anaesthesia with HHSD OCT in the supine position. Older cooperative retinoblastoma patients were additionally imaged with upright conventional OCT. Clinical data were derived from patient charts and from a prospectively maintained interinstitutional retinoblastoma database. Complementary imaging techniques, including RetCam™, fluorescein angiography and B-scan ultrasound, were assessed. RESULTS: Twenty-two intraocular lesions in 16 patients were imaged. HHSD OCT was used exclusively in 19 lesions, while conventional OCT was also performed in three cases. Small lesions were imaged in five cases, all of which were localised to the middle retinal layers. Clinical uses for HHSD OCT imaging identified included: diagnosis of new lesions, monitoring response to laser therapy and the identification of edge recurrences. CONCLUSIONS: Although indirect ophthalmoscopy remains the gold standard for diagnosis and treatment of retinoblastoma, HHSD OCT is a valuable tool in better understanding and managing retinoblastoma.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".