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Record W4353086273 · doi:10.54097/hset.v36i.6121

Monoclonal Antibodies in Cancer Immunotherapy

2023· article· en· W4353086273 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHighlights in Science Engineering and Technology · 2023
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMonoclonal antibodyImmunotherapyCancerMedicineAntigenAntibodyCancer immunotherapyTargeted therapyHybridoma technologyIn vivoCancer researchImmunologyBiologyBiotechnologyInternal medicine

Abstract

fetched live from OpenAlex

The technology of monoclonal antibodies (MABs) was originated from the ideas that how antibodies of the B cells bind to the specific antigens to protect the body against foreign invasion. Nowadays, MABs drugs have been approved for more than 30 targets, they have been considered as one of the first-line immunotherapies which specifically target certain cancers. Due to their exquisite target selectivity and less toxicity, MABs have become the new mainstay of pharmaceutical industry. This review summarizes the history of discovery, basic structure, and in vivo production of the MABs. Meanwhile, this review introduces the basic therapeutic applications, mechanism and working process of MABs, MABs and its targeted cancers, issues with current MABs technology and corresponding methods of improvements are also discussed. Overall, MABs do have broad prospect in the future since its effectiveness of treating cancers with poor prognosis and wide therapeutic applications.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
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 head0.019
GPT teacher head0.320
Teacher spread0.301 · 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