EXTH-25. OPTIMIZATION OF NEW CHEMOTHERAPEUTIC AGENTS BY INTRA-ARTERIAL INFUSION IN GLIOBLASTOMAS
Bibliographic record
Abstract
Abstract Being the most aggressive malignant primary brain tumor, glioblastoma (GBM) is a difficult disease to treat. The first line treatment is well established, but most patients will relapse before or during the treatments. The overall survival is only 14,6 months while the progression-free survival is 6,9 months. In Sherbrooke, Dr Fortin prioritize the intra-arterial (IA) administration of chemotherapeutic agents (CTA) as a second line treatment. Unfortunately, certain patients will not respond or will even develop chemoresistance to the agents. Therefore, there is a strong need for bringing more CTAs onto clinical care. The maximal tolerable dosage (MTD) of every agent was first determined. Every CTAs were then administered IA by retrograde infusion in the external carotid but also intravenously (IV) by the caudal vein. We used the Fischer-F98 glioma rat model in our studies. For the efficacy study, we implanted 10 000 F98 cells into their brain. After 10 days, they received the treatment associated to their group. The animals were followed every day and were euthanized when their condition was too deteriorated. The dosages found in the MTD study for topotecan and cytarabine was 4 mg/kg and 8,75 mg/kg respectively while paclitaxel was too toxic. As for the efficacy study, the IA group of topotecan showed a promising outcome with a higher survival rate (29 days) compared to the IV one (23,5 days). As for the cytarabine, the opposite was observed (IA: 22 days, IV: 26,5 days). While topotecan and cytarabine could be given intra-arterially, only topotecan showed conclusive results. More animals will be added to this group to confirm this outcome. Cytarabine is not a good option since the IV group survived a longer time than the IA group. As for the paclitaxel, the intra-tumoral infusion will be tested to see if we can get positive results.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".