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Record W2039687494 · doi:10.1038/mt.2009.283

Intelligent Design: Combination Therapy With Oncolytic Viruses

2009· review· en· W2039687494 on OpenAlexaff
Kathryn Ottolino‐Perry, Jean‐Simon Diallo, Brian D. Lichty, John C. Bell, J. Andrea McCart

Bibliographic record

VenueMolecular Therapy · 2009
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsMount Sinai HospitalToronto General HospitalMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsOncolytic virusVirotherapyRadiation therapyMedicineDiseaseModalitiesClinical trialCombination therapyCancerIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Metastatic cancer remains an incurable disease in the majority of cases and thus novel treatment strategies such as oncolytic virotherapy are rapidly advancing toward clinical use. In order to be successful, it is likely that some type of combination therapy will be necessary to have a meaningful impact on this disease. Although it may be tempting to simply combine an oncolytic virus with the existing standard radiation or chemotherapeutics, the long-term goal of such treatments must be to have a rational, potentially synergistic combination strategy that can be safely and easily used in the clinical setting. The combination of oncolytic virotherapy with existing radiotherapy and chemotherapy modalities is reviewed along with novel biologic therapies including immunotherapies, in order to help investigators make intelligent decisions during the clinical development of these products.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.092
GPT teacher head0.381
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations209
Published2009
Admission routes1
Has abstractno

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