Bibliographic record
Abstract
PURPOSE OF REVIEW: Conventional surgery, radio- and chemotherapy have failed to significantly improve the prognosis of patients with malignant astrocytomas--hence the need for understanding their molecular biology. Harvesting this understanding to yield novel biological targeted therapies has approached the clinical doorstep. Therapeutic efficacy will likely require combinatorial therapy involving biologicals and conventional therapies, with small incremental efficacy in selected sub-groups. This review highlights some of the findings over the past year (June 2003-2004) that have contributed to this slow but essential journey towards our understanding of the biology of astrocytomas. RECENT FINDINGS: The accumulation of loss and/or gain of function molecular alterations underlying astrocytoma formation, progression and key growth parameters including proliferation, angiogenesis, apoptosis, invasion and resistance are emerging. These alterations involve those regulating the growth factor/receptor and downstream signaling networks, cell cycle, immune modulators and other key biological processes. The advances are facilitated by interactions amongst clinician and basic scientists, in both academia and industry. They have incorporated high-throughput bioinformatics analysis of genomic and expression array data, the emerging field of proteomics and development of various genetically engineered models of astrocytomas. SUMMARY: Astrocytomas, like other cancers, are a result of several molecular alterations, some of which strongly correlate to their pathological grade. However, molecular heterogeneity exists between astrocytomas of similar grades and likely between varying micro-environmental regions of a single tumor. Characterization of the molecular signature of an astrocytoma and linking with the appropriate 'tailored' therapie(s) is the hope of the future.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".