59 THE BC RESEARCH CONSORTIUM OF NEURO-ONCOLOGY (RECON)
Bibliographic record
Abstract
Abstract High-grade gliomas (HGGs) are deadly primary brain tumours in pediatric and adults with the lowest survival rates in allcancer (< 10%). The Research Consortium for Neuro-oncology (ReCoN) is a team consist of clinicians and researchers in BC Cancer and Vancouver General Hospital aiming to identify disease mechanisms of poorly defined “histone H3- and IDH-WT” HGGs. The study aims to accelerate the derivation of patient-specific avatar glioma models, agnostically nominate therapeutic targets through genomics, and to empirically evaluate therapeutic efficacy/cytotoxicity using patient-derived cell models. As a proof-of-concept study, 12 primary resected grade IV glioblastoma tumour and tumour-derived cell models will undergo matched molecular profiling, including long read whole genome sequencing and single cell ATAC- and RNA-seq. Computational tools such as GRETTA will be used to nominate targetable genetic network vulnerabilities via small molecule compounds. The nominated compounds will be evaluated for efficacy/cytotoxicity using patient-derived models. Through this pilot, we aim to develop an end-to-end workflow to enhance the detection of driver mutations and design of driver-specific therapies in “WT” HGGs to improve patient-specific care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.067 | 0.034 |
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".