NCMP-10. DEXAMETHASONE: A TOOL AND A PROBLEM IN GLIOBLASTOMA MANAGEMENT
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) patient morbidity and overall survival (OS) are associated with dexamethasone use. We studied dexamethasone-associated hyper-glycemia, leukocytosis, and accompanying complications in the peri-operative period. We also examined the effect of metformin treatment on GBM patient OS. We retrospectively studied 243 GBM patients admitted between 2014-2018. Patients with peri-operative glucose measurements and adequate follow-up to assess for complications were included. Metformin was used for treatment of diabetes or in the management of glucocorticoid-induced hyper-glycemia. Kaplan-Meier curves and log-rank p test were used for univariate analysis. Cox-proportional hazards model was used to generate adjusted hazard ratios for multivariate analysis. Dexamethasone dose was associated with hyper-glycemia and leukocytosis post-operatively. Poor glycemic control was associated with increased odds of 30-day any complication on univariate analysis and 30-day any complication and increased length of stay on multivariate analysis. We then demonstrated that metformin improved OS in methylated O6-methylguanine DNA methyltransferase gene (MGMT) promoter tumor patients (652 days, 95% CI 327 – 897, n = 25 vs 394 days, 95% CI 210 – 513, n = 63; P = .049). Non-diabetic MGMT-methylated tumor patients treated with metformin also had an improved median OS of 736 days, 95% CI 365 – 1036, n = 11 vs 395 days, 95% CI 210 – 513, n = 66; P = .01). Overall, peri-operative hyper-glycemia and higher average dexamethasone use are associated with poor glycemic control and leukocytosis. As a result, dexamethasone can increase the risk of peri-operative complications in GBM patients. Avoiding hyper-glycemia by limiting dexamethasone use or using Metformin may decrease the risk of complications and improve OS.
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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 | 0.003 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".