Reovirus-based therapy for cancer
Bibliographic record
Abstract
Reovirus is an oncolytic virus that is not associated with significant disease in humans, but is selectively able to replicate in cancer cells through exploitation of abnormal Ras signaling. Pre-clinical studies have demonstrated that treatment with reovirus is associated with significant anticancer activity across a range of tumor types. Reolysin is a proprietary formulation of the human reovirus developed by Oncolytics Biotech. Clinical evaluation of reovirus therapy has shown that it is well tolerated when administered locally or systemically. Encouraging anticancer efficacy has been observed with single-agent treatment and in combination with chemotherapy and radiotherapy. Phase II studies are currently evaluating reovirus alone and in combination with standard therapy in an array of tumor types. While immune sensitization hinders the anticancer efficacy of reovirus, it is important in preventing systemic toxicity. Immunosuppressive strategies are being developed that reduce immune neutralization of the virus to allow for improved tumor penetration, but retain sufficient antibody levels to protect normal tissues. The lack of toxicity and promising efficacy of reovirus has raised hopes that it will become an established anticancer agent.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".