MEMORANDUM FOR: Science Writers and Editors on the Journal Press List: Reovirus Shows Promise Against Brain Tumors
Bibliographic record
Abstract
June 14, 2001 (EMBARGOED FOR RELEASE 4 P.M. ET June 19) New research shows that reovirus has potent anticancer effects against glioma (a type of brain cancer) cells growing in culture and against human gliomas growing in mice. Malignant gliomas are highly aggressive, invasive, and resistant to available treatments. The median survival is only 1 year, and long-term survivors are very rare. However, it is known that reovirus can “take over” activated Ras-signaling pathways in malignant cells, leading to cell destruction and offering hope as a treatment for gliomas and other cancers. The reovirus (Respiratory Enteric Orphan) used in this study is a double-stranded RNA virus commonly isolated from the human respiratory and gastrointestinal tract. It infects and destroys tumor cells but not normal cells, and it does not cause disease in humans. The results of reovirus testing against gliomas are presented by Peter Forsyth, M.D., and colleagues at the University of Calgary, Canada, in the June 20 issue of the Journal of the National Cancer Institute.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.066 | 0.049 |
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".