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Record W4394175000 · doi:10.6084/m9.figshare.21608186

Chemotherapy plus panitumumab/cetuximab versus chemotherapy plus bevacizumab in wild-type KRAS/RAS metastatic colorectal cancer: a meta-analysis

2022· dataset· en· W4394175000 on OpenAlexaff
Chengren Zhang, Lili Liu, Yaochun Lv, Jingjing Li, Cong Cao, Jiyong Lu, Shuai Wang, Binbin Du, Xiongfei Yang

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldMedicine
TopicColorectal Cancer Treatments and Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPanitumumabCetuximabBevacizumabKRASMedicineColorectal cancerOncologyChemotherapyInternal medicineMeta-analysisCancer

Abstract

fetched live from OpenAlex

It remains controversial which targeted monoclonal antibodies combined with chemotherapy can provide better efficacy in wild-type KRAS/RAS metastatic colorectal cancer (mCRC) patients. Therefore, we used this meta-analysis to assess the latest evidence of clinical outcomes. We systematically searched PubMed, Web of Science, Cochrane Library and Embase databases for eligible studies published from database inception to May 2022. RevMan 5.4 was used to conduct the meta-analysis. 11 RCTs involving a total of 3575 patients were included. Meta-analysis showed that EGFR inhibitors significantly prolonged the overall survival (OS) [HR = 0.83, 95%CI (0.73, 0.94), P = 0.003] and overall response rate (ORR) [RR = 1.11, 95%CI (1.05, 1.18), P = 0.0003] compared to VEGF inhibitors in wild-type KRAS/RAS mCRC patients, but no significant difference in progression-free survival (PFS) [HR = 0.96, 95%CI (0.87, 1.07), P = 0.50]. In subgroup analysis, the survival benefit of EGFR inhibitors was limited to first-line treatment. Our study showed that EGFR inhibitors were superior to VEGF inhibitors in wild-type KRAS/RAS mCRC patients, especially in patients with first-line treatment. However, subsequent large sample, multi-center RCTs are needed to further verify our conclusions.

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)0.012
metaresearch head score (Gemma)0.018
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: Dataset · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.018
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0180.065
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.110
GPT teacher head0.363
Teacher spread0.253 · 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
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

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