Survivorship from pediatric and adult brain tumors: The 2024 Brain Tumor Epidemiology Consortium meeting report
Bibliographic record
Abstract
Abstract The Brain Tumor Epidemiology Consortium (BTEC) is an international organization with membership of individuals from the scientific community with interests related to brain tumor epidemiology, including surveillance, classification, methodology, etiology, and factors associated with morbidity and survival. The 2024 annual BTEC meeting entitled “Survivorship from Pediatric and Adult Brain Tumors” was held in Mainz, Germany, USA, on May 15–17, 2024. The meeting gathered scientists from Africa, Australia, Europe, and North America and included 4 keynote sessions focusing on brain tumor survivorship across the age spectrum. The meeting included 3 abstract sessions, which also included scientific talks around brain tumor risk factors and predicting risk and survival. We also held a brainstorming session to form a near-term research strategy around brain tumor survivorship in the epidemiology community. This report provides a summary of the meeting content.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".