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Record W3038892567 · doi:10.1159/000507816

Rechallenge Strategy in Cancer Therapy

2020· review· en· W3038892567 on OpenAlexaff
Ekaterina Hanovich, Tim Asmis, Michael Ong, David J. Stewart

Bibliographic record

VenueOncology · 2020
Typereview
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineClinical trialDiseaseImmunotherapyCancerIntensive care medicineDrugChemotherapyDrug resistanceOncologyTargeted therapyLimitingInternal medicinePharmacology

Abstract

fetched live from OpenAlex

Drug resistance is one of the most important factors limiting the success of systemic anticancer therapy in achieving cure or prolonged overall survival. In clinical practice, resistant disease describes cancer that is found to have progressed since the time of treatment initiation. The term "drug resistant" is often used synonymously with "progressive disease" when referring to a treated tumour. Stopping a treatment at the time of disease progression is the current dominant approach of clinical trial conduct; therefore, available data from clinical trials are routinely not able to provide any information that could challenge this concept of permanent drug resistance. However, drug rechallenge and treatment continuation beyond progression have emerged as potential strategies in the past decade, especially for molecularly targeted agents and immunotherapy. In this review we focussed on rechallenge strategies for chemotherapy, immune therapy and targeted therapy in the main types of cancer.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.235
GPT teacher head0.478
Teacher spread0.243 · 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 designNot applicable
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

Citations19
Published2020
Admission routes1
Has abstractyes

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