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Record W2611322156 · doi:10.15173/m.v1i26.948

Chimeric Antigen Receptors: The Future of Cancer Immunotherapy

2014· article· en· W2611322156 on OpenAlexvenueno aff
Adam Eqbal, Ben Li

Bibliographic record

VenueThe Meducator · 2014
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsnot available
Fundersnot available
KeywordsChimeric antigen receptorImmunotherapyCancer immunotherapyCancerReceptorImmunologyMedicineAntigenImmune systemInternal medicine

Abstract

fetched live from OpenAlex

Chemotherapy, radiation therapy, and surgery are currently the most widelyused cancer treatment modalities. Although these therapies have successfullybeen used to treat various cancers, they are accompanied by significantlimitations. Cancer immunotherapy, an emerging field of study named thebreakthrough of the year in 2013 is offering hope for an era of new anticancermodalities.1 In addition to serving as a safeguard against infectiousdisease, the immune system also prevents and delays tumour development..Scientists have developed various immunotherapeutic treatments, eachtargeting different aspects of the immune system. One therapy involvesgenetically engineering T lymphocytes with chimeric antigen receptors (CARs)that enhance the ability of T-cells to recognize and eliminate cancer cells. CART-cell therapy has been highly effective in several clinical trials, eliminatingdetectable tumour mass in several patients with B-cell malignancies andsolid tumours.2 This article will provide background into the field of cancerimmunotherapy, the structure and function of CARs, and recent developmentsin CAR-T-cell therapy.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.869
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0070.000

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 head0.015
GPT teacher head0.333
Teacher spread0.318 · 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 designNot applicable
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
Published2014
Admission routes1
Has abstractyes

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