Li-Fraumeni Syndrome Cancer Surveillance Strategy Considerations for Glioblastoma Multiforme
Bibliographic record
Abstract
Sporadic or inherited deficiencies in the production or activity of the tumor suppressor P53 lead to Li-Fraumeni Syndrome (LFS), a multi-organ tumorigenic condition. Glioblastoma multiforme (GBM), a tumor that commonly presents with a median age of 64, has a higher chance of appearing in much younger patients who have LFS [9]. Since the implementation of the 2016 Toronto Protocol to increase cancer surveillance in LFS patients, three cases of LFS-GBM have been discussed [11-13]. Here, we report a case of LFS in an 18-year-old male who had a seizure due to a GBM that had evaded a full-body MRI six months prior. Furthermore, we discuss the potential quality of life (QOL) benefits of providing patients with a shorter brain MRI screening interval: better survival outcomes and peace of mind. Though there may be a rise in the financial cost with an increase in the number of MRI scans, the prevalence of aggressive tumors that must be treated early for a better prognosis warrants more frequent screening. Furthermore, we address the importance of expanding clinical knowledge on GBM in the LFS setting as well as addressing the benefits of the protocol through statistical studies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".