EPID-10. EVOLVING SURVIVAL TRENDS IN INFANTS WITH BRAIN TUMORS: A LONGITUDINAL POPULATION BASED STUDY
Bibliographic record
Abstract
Management of brain tumors in infants has evolved over time, however, historical prognostication does not reflect current treatment and survival trends. This study used a population-based approach to differentiate contemporary from historical survival curves to provide up-to-date prognostication. Observational cohort analysis was performed using the Surveillance, Epidemiology and End Results (SEER) database. Infants with brain tumors from 1973-2013 were divided by tumor subtype (diffuse astrocytic/oligodendroglioma, choroid plexus, embryonal, ependymal, medulloblastoma and pilocytic astrocytoma). The 1, 5 and 10 year survival was stratified by decade. Trends in management and outcomes were analyzed. Between 1973 and 2013, 2996 patients <24 months were identified. All tumor types except embryonal and choroid plexus demonstrated improved survival with time. Infants with embryonal tumors showed a decline in survival between the 1970s and 1990s (p= 0.008). Infants with choroid plexus tumors had no change in survival over time. The greatest improvement in survival was seen for infants with ependymal tumors, with 5-year survival improving from 28% (95% CI 15-42%) in the 1980s to 77% (95% CI 69-83%) the 2000s. Radiation therapy declined from 1970 to 2000 for all tumors, however, radiation treatment for embryonal and ependymal subtypes has increased since 2000. Despite improved overall survival from the 1970s onwards, certain subtypes of infant brain tumors have not matched this trend. The use of radiation has declined, although in specific tumor types, its use has been associated with better survival. Prognostication in infants with brain tumors should be updated to reflect current treatment trends.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".