Bibliographic record
Abstract
Gliomas, that do not respond to alkylating agent chemotherapy, can be made more sensitive to chemotherapy through promotor mediated epigenetic silencing of the MGMT gene. MGMT is one of the important markers in glioblastomas as it not only predicts response to therapy but may also be used as an independent prognostic marker. As such, MGMT is gaining increasing traction in diagnosis, prognostication, and therapeutic decision-making for these highly malignant gliomas. Although, MGMT promotor methylation status is becoming more commonly used in neuro-oncology; this test remains imperfect. Because of its increasing use in clinical practice and research, it is integral that we are aware of its pitfalls and complications. Currently, there are many ways to detect a patient's MGMT promotor methylation status, including: quantitative PCR, methylation-specific PCR, pyrosequencing, real time PCR with high resolution melt, and the infinitum methylation EPIC beadChip. The technical aspects, shortcomings, and optimal approach to interpreting the results of each method will be discussed. Furthermore, given that none of these methods have been prospectively validated, the challenge of equivocal cases will be discussed, and technical and logistic strategies for overcoming these challenges will be proposed. Finally, the difficulty in validating these methods, establishing standardized practice, and considerations of the cost of these competing methods will be explored.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".