Pediatric low-grade gliomas: next biologically driven steps
Bibliographic record
Abstract
Despite the fact that they are not typically life-threatening, low-grade gliomas (LGGs) remain a significant clinical challenge in pediatric neuro-oncology due to comorbidities associated with these tumors and/or their treatments, and their propensity to multiply recurs. LGGs, in total the most common brain tumors arising in childhood, can often become a chronic problem requiring decades of management. The Second International Consensus Conference on Pediatric Low-Grade Gliomas held in Padua, Italy in 2016 was convened in an attempt to advance the pace of translating biological discoveries on LGGs into meaningful clinical benefit. Topics discussed included: the implications of our growing biological understanding of the genomics underlying these tumors; the assessment of the model systems available; the implications of the molecular and histopathologic differences between adult and pediatric diffuse gliomas; and steps needed to expedite targeted therapy into late-stage clinical trials for newly diagnosed cases. Methods for the diagnostic assessment of alterations in the Ras/mitogen-activated protein kinase pathway, typical for these tumors, were also considered. While the overall tone was positive, with a consensus that progress is being and will continue to be made, the scale of the challenge presented by this complex group of tumors was also acknowledged. The conclusions and recommendations of the meeting panel are provided here as an outline of current thinking and a basis for further discussion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".