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Record W2604482977 · doi:10.1016/j.adro.2017.03.007

4-1BB (CD137) and radiation therapy: A case report and literature review

2017· article· en· W2604482977 on OpenAlexaff
Jay Shiao, Nathan L. Bowers, Tahseen H. Nasti, Faisal Khosa, Mohammad K. Khan

Bibliographic record

VenueAdvances in Radiation Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineMedical physicsRadiation therapyInternal medicine

Abstract

fetched live from OpenAlex

SummaryThere has yet to be any clinical case studies demonstrating the efficacy, safety, and response of combined therapy with 4-1BB agonists and radiation. This report provides the first case of immune costimulatory directed therapy potentially augmented with RT after progression of disease despite several treatments with immunotherapy. Based on preclinical data and results presented in this case, radiation combined with 4-1BB agonists may have a unique and potent effect in addition to other therapies. There has yet to be any clinical case studies demonstrating the efficacy, safety, and response of combined therapy with 4-1BB agonists and radiation. This report provides the first case of immune costimulatory directed therapy potentially augmented with RT after progression of disease despite several treatments with immunotherapy. Based on preclinical data and results presented in this case, radiation combined with 4-1BB agonists may have a unique and potent effect in addition to other therapies.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.004
Science and technology studies0.0020.002
Scholarly communication0.0020.005
Open science0.0020.002
Research integrity0.0070.004
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.019
GPT teacher head0.397
Teacher spread0.378 · 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 designCase report
Domainnot available
GenreEmpirical

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

Citations4
Published2017
Admission routes1
Has abstractyes

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