Bibliographic record
Abstract
Glioblastoma multiforme are the most common and lethal brain tumor in adults. Several factors including tumor heterogeneity, the presence of GBM cancer stem cell, and the complexity of the mechanisms of GBM pathogenesis make it difficult to arrive at a definitive treatment for patients diagnosed with this lethal disease. The local control of the tumor remains an issue and the optimal radiation schedule still remains undefined. In my thesis work, I compiled a GBM database from all patients treated at the McGill University Health Center between 2005 and 2013. We explored whether hypofractionation can be used as a radiation regimen alternative to the current standard of care of a more prolonged radiation treatment. We found that a treatment of 60 Gy in 20 fractions constitutes a safe radiation approach that shows survival comparable to a standard radiation regimen while allowing for a shorter treatment time. Furthermore, because most tumors recur in less than a year, it became apparent to us that the patients in our population who underwent repeat surgery had a better outcome than those who did not. We aimed to assess the benefits of re-operation and salvage therapies (chemotherapy and/or re-irradiation) for recurrent GBM and to identify the prognostic factors associated with better survival. We found that re-operation for recurrent GBM provides survival prolongation of about 4 months from the time of progression with acceptable toxicity. My Master's thesis explores the multiple challenges involved in achieving a cure for GBM patients. We also present here our research findings from our patient population treated for GBM in the form of two manuscripts, including one that has been published in the Radiation Oncology journal earlier this year, and another being submitted to the Journal of Neuro-oncology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".