Asynchronous pineoblastoma is more likely after early diagnosis of retinoblastoma: a meta‐analysis
Bibliographic record
Abstract
PURPOSE: To determine the risk of patients with an early diagnosis of heritable retinoblastoma being diagnosed with TRb (or pineoblastoma) asynchronously in a later stage and its effect on screening. METHODS: We updated the search (PubMed and Embase) for published literature as performed by our research group in 2014 and 2019. Trilateral retinoblastoma (TRb) patients were eligible for inclusion if identifiable as unique and the age at which TRb was diagnosed was available. The search yielded 97 new studies. Three new studies and eight new patients were included. Combined with 189 patients from the previous meta-analysis, the database included 197 patients. The main outcome was the percentage of asynchronous TRb in patients diagnosed before and after preset age thresholds of 6 and 12 months of age at retinoblastoma diagnosis. RESULTS: Seventy-nine per cent of patients with pineoblastoma are diagnosed with retinoblastoma before the age of 12 months. However, baseline MRI screening at time of retinoblastoma diagnosis fails to detect the later diagnosed pineal TRb in 89% of patients. We modelled that an additional MRI performed at the age of 29 months picks up 53% of pineoblastomas in an asymptomatic phase. The detection rate increased to 72%, 87% and 92%, respectively, with 2, 3 and 4 additional MRIs. CONCLUSIONS: An MRI of the brain in heritable retinoblastoma before the age of 12 months misses most pineoblastomas, while retinoblastomas are diagnosed most often before the age of 12 months. Optimally timed additional MRI scans of the brain can increase the asymptomatic detection rate of pineoblastoma.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.035 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".