Oncolytic Viruses: Therapeutics With an Identity Crisis
Bibliographic record
Abstract
Oncolytic viruses (OV) are replicating viral therapeutics for the treatment of cancer and have been in laboratory development for about twenty years. Recently, the FDA approved Imlygic, a herpes virus based therapeutic for the treatment of melanoma and thus OVs have entered a new era where they are a weapon in the armament of the oncologist. OVs are unique therapeutics with multiple mechanisms of therapeutic activity. The exact path for their development and eventual uptake by pharmaceutical companies is somewhat clouded by an uncertain identity. Are they vaccines, tumour lysing therapeutics, inducers of innate immunity, gene therapy vectors, anti-vascular agents or all of the above? Should they be developed as stand-alone loco-regional therapeutics, systemically delivered tumour hunters or immune modulators best tested as combination therapeutics? We summarize data here supporting the idea, depending upon the virus, that OVs can be any or all of these things. Pursuing a "one-size fits all" approach is counter-productive to their clinical development and instead as a field we should build on the strengths of individual virus platforms.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".