How long should survivors of pediatric medulloblastoma and ependymoma be screened for recurrence? A retrospective cohort study
Bibliographic record
Abstract
Recurrence is the most common cause of late mortality in pediatric brain tumor survivors. However, it is unclear how long such patients should be monitored with periodic neuroimaging. Therefore, we investigated the utility of neuroimaging surveillance for recurrence ≥5 years post diagnosis in survivors of pediatric medulloblastoma and ependymoma. We conducted a retrospective study of survivors of medulloblastoma or ependymoma treated between 2000 and 2017. Eligible survivors were disease-free 5 years after diagnosis and underwent magnetic resonance imaging surveillance ≥5 years after diagnosis. Medulloblastoma survivors with a history of recurrence <5 years after diagnosis were excluded. Of 302 children diagnosed in the study period, 129 met inclusion criteria (89 medulloblastoma/40 ependymoma; 77 (59.7%) male; median age at diagnosis 6 years (range < 1-13); median time from diagnosis to last scan 134 months (61-283)). Four medulloblastoma patients had late recurrent disease, one of which was detected on routine neuroimaging (asymptomatic). All medulloblastoma patients with late recurrence died, except for one previously unirradiated patient who was disease-free 29 months after recurrence. Nine ependymoma patients had late recurrence of which 7 were detected on routine neuroimaging. Six out of seven asymptomatic late recurrent ependymoma patients remain alive with a median time after recurrence of 45.5 months (range: 3-121). Both symptomatic patients died. Among ependymoma survivors, asymptomatic detection of late recurrence by surveillance neuroimaging was associated with better survival than symptomatic detection, supporting the continuation of surveillance for at least 10 years after diagnosis. The benefit of prolonged surveillance in medulloblastoma survivors remains uncertain.
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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 | 0.005 |
| 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 | 0.000 |
| 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".