Detailed characterization of partial tumor resection in the Syngeneic Fischer/F98 Glioma Model
Bibliographic record
Abstract
BACKGROUND: Preclinical models of brain tumors play a fundamental role in understanding tumor biology and deploying anti-tumor strategies. However, preclinical studies evaluate their potential therapy in tumor model without prior resection. Nevertheless, maximal safe resection, the first step in the clinical treatment of glioblastoma (GBM), is known to have a significant effect on adjuvant treatments. NEW METHOD: We have therefore characterized two techniques to perform tumor resection in F98 glioma-bearing rats to bring this model closer to the clinical context. A total of 65 animals were assigned in 5 different groups: control, catheter (1.74 mm diameter) and biopsy punch (1.5/ 2.5/ 3 mm diameter). On day 10 post-tumor implantation, some animals were sacrificed on day 11 for histological analysis whereas the remaining animals were used for survival estimates. RESULTS: All animals in the survival groups that underwent tumor resection recurred. The resection cavities were visible on the H&E histological sections. No significant difference was observed between the control and resection groups in term of survival but there was a trend towards improved survival with increasing tool diameter. COMPARISON WITH EXISTING METHODS: Few studies have investigated the development of tumor resection models, but the majority of these techniques require sophisticated equipment. To our knowledge, we are the first to develop an easy-to-perform partial tumour resection model using the Fischer-F98 glioma model. CONCLUSIONS: Here we present a detailed characterization of the tumor resection procedure and recurrence model, which has potential for the investigation of local delivery strategies in the treatment of GBM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 teacher head, 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".