PC3 - 152 Impact of Extent of Resection Upon Outcome in Newly Diagnosed Glioblastoma: A Study in the Molecular Era
Bibliographic record
Abstract
For decades, debate has persisted regarding the role of surgical resection in newly diagnosed glioblastoma. There is increasing evidence that extent of resection (EoR) is an independent prognostic factor. Previous work has proposed the inclusion of EoR in a risk stratification algorithm but does not incorporate account recent advances in the molecular characterization of tumours. We set out to investigate the effect of EoR on overall survival (OS), and to develop a stratification algorithm incorporating both EoR and modern molecular markers for prognostication. HYPOTHESIS: Greater EoR is independently associated with improved OS. METHODS: We examined 190 consecutive cases of histopathologically confirmed newly-diagnosed glioblastoma who were operated upon between January 1, 2012 and December 31, 2014. Variables including age, sex, postal code, KPS, tumour location, presenting symptoms, treatment history, date of progression, date of reoperation, as well as MGMT, IDH, 1p/19q codeletion, and ATRX status were recorded. Preoperative and postoperative MRIs were reviewed and volumetric tumour burden will be analyzed and EoR will be calculated. RESULTS: Preliminary EoR calculations (n=18) show a positive correlation between EoR and OS. CONCLUSION: A correlation exists between EoR and OS, although multivariable analysis is planned to exclude potential confounders. MRI review, chart review including molecular marker analysis and EoR calculations are ongoing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".