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Record W2013336468 · doi:10.1517/14712598.8.9.1381

Development of targeted oncolytic virotherapeutics through translational research

2008· review· en· W2013336468 on OpenAlexaff
Ta‐Chiang Liu, Tae-Ho Hwang, John C. Bell, David H. Kirn

Bibliographic record

VenueExpert Opinion on Biological Therapy · 2008
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsJennerex Biotherapeutics (Canada)
Fundersnot available
KeywordsOncolytic virusTranslational researchClinical trialNew product developmentMedicineComputer scienceVirologyVirusBusiness

Abstract

fetched live from OpenAlex

BACKGROUND: Oncolytic virotherapeutics is a promising platform for cancer treatment but the product class has yet been successful. The key to success is integration of bidirectional translational research to rapidly address issues encountered in the laboratory and the clinics. OBJECTIVE: We highlight the hurdles identified for the targeted oncolytic virotherapy approach, specifically those identified in clinical trials with wild-type viruses and first-generation targeted agents. We also analyze the translational research and development that has been applied to overcome these hurdles, including virus engineering and design improvements for next-generation virotherapeutics. RESULTS/CONCLUSION: The iterative loop between the clinic and the lab can function as a major driving force to optimize products from this platform.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.413
GPT teacher head0.504
Teacher spread0.091 · 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 teacher head, not a consensus.

Study designOther design
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

Citations11
Published2008
Admission routes1
Has abstractyes

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