Bibliographic record
Abstract
Abstract Based on recent discoveries and data integration in genomics and epigenomics, a lot more is known about the biology of childhood brain tumors today when compared to a decade ago. This has vastly benefitted brain tumor diagnostics, including classification and some predictive and prognostic biomarkers. Yet the gap between this newly defined diagnostic gold standard and routine clinical implementation largely remains to be bridged. Additionally, the cell of origin for a majority of brain tumor entities remains elusive. The increased granularity of understanding intertumor heterogeneity also comes with the challenge of accounting for this heterogeneity in our therapeutic approaches. Ever-smaller patient groups and, on the other hand, an ever-increasing portfolio of potentially available drugs, require a paradigm shift in how we run and evaluate preclinical and clinical trials. Academically rewarding proof-of-concept studies that are typically well published, which certainly have a different purpose and thus lack the rigor of thorough preclinical filtering (because they typically embark on one or two “representative” models, which certainly does not cover the heterogeneity of a disease by any means), should no longer be regarded “sufficient preclinical evidence” to justify a clinical trial. Thus, novel incentives and funding models for this “less academically attractive,” but highly patient-relevant studies need to be explored. Phase I trials just with the purpose of dose finding should not be carried without a path forward into efficacy testing. Drugs with no evidence for brain penetration should not “just” be tried in patients with brain tumors without even making the best effort to understand brain and tumor penetrance. Academic work-up of biologic material from every single patient being enrolled on a trial should be mandatory rather than “nice to have.” Hereditary cancer predisposition should no longer be ignored when it comes to treatment stratification. Reimbursement models for “next-generation diagnostics,” preclinical phase I and phase II studies, and academic clinical trials (focusing on combinations rather than monotherapies in end-stage disease) have to shift from traditional third-party funding to alternative funding models. If we do not manage to address this paradigm shift, high-risk brain tumor patients may still mostly die from their disease 10 years from now. Citation Format: Stefan M. Pfister. Pediatric neuro-oncology: What’s next? [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr IA01.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.045 | 0.038 |
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".