RETINAL DETACHMENT AND RETROBULBAR CYSTS IN A LARGE COHORT OF OPTIC NERVE COLOBOMA
Bibliographic record
Abstract
PURPOSE: To examine the relationship between retinal detachment and retrobulbar cysts in patients with optic nerve coloboma (ONC) and Morning Glory syndrome (MGS). METHODS: Patients diagnosed with either ONC or MGS were identified through a search of the Sick Kids database. Seventy-one patients either agreed to come in for a B-scan or had an incidental orbital B-scan or magnetic resonance imaging or both. Eyes with orbital B-scan ultrasound and/or magnetic resonance imaging images were assessed independently by two ophthalmologists and a radiologist for the presence of retrobulbar cysts. Retinal detachment was identified clinically with either indirect ophthalmoscopy or from fundus photographs. RESULTS: Forty-five of 71 (63%) and 26/71 (37%) patients had ONC and MGS, respectively. Retinal detachment occurred significantly more often in eyes with MGS than with ONC (9/17 [53%] vs. 5/45 [11%], P = 0.03, respectively). Retrobulbar cysts were not detected more often in MGS than in ONC (11/45 [24%] vs. 7/26 [27%]; P = 1.0). Eyes with retrobulbar cysts were significantly more likely to be associated with retinal detachment than those without (7/18 [39%] vs. 7/53 [13%]; P = 0.04). CONCLUSION: Retinal detachment occurs more frequently in MGS than in ONC in a cohort of patients referred to a specialist children's retinal service. Eyes with retrobulbar cysts are more likely to be associated with retinal detachment.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".