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Record W4293582013 · doi:10.3389/fphar.2022.914683

Efficacy, safety, and cost-effectiveness analysis of aflibercept in metastatic colorectal cancer: A rapid health technology assessment

2022· review· en· W4293582013 on OpenAlexaboutno aff
Pu Ge, Ning Wan, Xiao Han, Xinpei Wang, Jinzi Zhang, Xiaoyi Long, Xiaonan Wang, Ying Bian

Bibliographic record

VenueFrontiers in Pharmacology · 2022
Typereview
Languageen
FieldMedicine
TopicColorectal Cancer Treatments and Studies
Canadian institutionsnot available
FundersUniversidade de Macau
KeywordsAfliberceptMedicineColorectal cancerOncologyCochrane LibraryFOLFIRIInternal medicineBevacizumabIntensive care medicineCancerRandomized controlled trialIrinotecanChemotherapy

Abstract

fetched live from OpenAlex

Background: Metastatic colorectal cancer (mCRC) imposes a heavy tumor burden worldwide due to limited availability of therapeutic drugs. Aflibercept, a kind of recombinant protein of the anti-vascular endothelial growth factor (VEGF) family, has been approved in clinical application among mCRC patients since 2012. A comprehensive analysis of the efficacy, safety, and cost-effectiveness of aflibercept in mCRC treatment is necessary. Objective: To evaluate the efficacy, safety, and cost-effectiveness of aflibercept for the treatment of mCRC in order to provide a decision-making reference for the selection of targeted drugs for second-line treatment of mCRC in Hong Kong, Macao, and Taiwan regions of China and the selection of new drugs for medical institutions in these regions. Methods: A systematic retrieve on databases including PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang, and Weipu, as well as relevant websites and databases of health technology assessment including the National Institute of Health and Clinical Optimization, Centre for Evaluation and Communication at the University of York, and the Canadian Agency for Medicines and Health Technology, was conducted. The literature was screened according to the inclusion and exclusion criteria, and data were extracted and analyzed by two authors, while the quality of the literature was assessed. Results: Finally, we included two HTA reports, 11 systematic reviews/meta-analyses, and two cost-effectiveness studies in the rapid health technology assessment. For mCRC patients receiving second-line treatment, aflibercept combined with FOLFIRI significantly increased progression-free survival (PFS) and overall survival (OS) and the objective response rate (ORR) also improved, compared with folinic acid + fluorouracil + irinotecan (FOLFIRI). In terms of safety, mCRC patients who received aflibercept combined with FOLFIRI therapy had a higher incidence of grade 3–4 adverse events than those who received FOLFIRI alone, including anti-VEGF–related adverse events (hypertension, hemorrhagic events, and proteinuria) and chemotherapy-related adverse events (diarrhea, weakness, stomatitis, hand-foot syndrome, neutropenia, and thrombocytopenia). In terms of cost-effectiveness, two economic studies conducted in the United Kingdom and Japan, respectively, found that compared with FOLFIRI, aflibercept combined with FOLFIRI had no cost-effectiveness advantage in mCRC patients receiving second-line treatment. Conclusion: Compared with FOLFIRI treatment, aflibercept combined with FOLFIRI for the second-line treatment of mCRC patients has better efficacy, worse safety, and is not cost-effective. More high-quality clinical studies are required for further exploration of aflibercept’s clinical value. Medical institutions in Hong Kong, Macao, and Taiwan regions of China should be cautious when using or introducing aflibercept plus FOLFIRI as a mCRC treatment.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.065
metaresearch head score (Gemma)0.134
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.065
Threshold uncertainty score0.344

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0650.134
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0130.032
Bibliometrics0.0290.018
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.057
GPT teacher head0.448
Teacher spread0.390 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Other design
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

Citations5
Published2022
Admission routes1
Has abstractyes

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