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Record W3006471402 · doi:10.5430/jst.v10n1p7

Immune checkpoint inhibitors for the treatment of glioblastoma: Where we are

2020· article· en· W3006471402 on OpenAlexvenueno aff
Bryan Lu, Senxi Du, Xiao–Tang Kong

Bibliographic record

VenueJournal of Solid Tumors · 2020
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineImmune checkpointImmune systemCytotoxic T cellMelanomaImmunotherapyCancerCancer researchPD-L1Lung cancerGlioblastomaImmunologyOncologyInternal medicineBiologyIn vitro

Abstract

fetched live from OpenAlex

Despite a history of frequent challenges and roadblocks, there has been recent excitement in the treatment of human cancer, specifically regarding the remarkable efficacy of various immune checkpoint inhibitors including programmed cell death protein 1 (PD-1), programmed cell death ligand 1 (PD-L1) and cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) blockers in treating metastatic melanoma, non-small cell lung cancer, and other malignant growths. However, treatment of glioblastoma multiforme (GBM) with immune checkpoint inhibitors so far has not been shown to be as successful in several randomized clinical trials as in other cancer with the exception of one pilot study that found promising results by neoadjuvant administration of Pembrolizimab for the treatment of recurrent GBM. Our article will review the current status of immune checkpoint inhibitors for the treatment of GBM.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.278
Teacher spread0.256 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2020
Admission routes1
Has abstractyes

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