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Record W3130058826 · doi:10.1039/d0fo03341c

Efficacy of ginseng and its ingredients as adjuvants to chemotherapy in non-small cell lung cancer

2021· review· en· W3130058826 on OpenAlexaff
He Zhu, Hui Liu, Jin‐Hao Zhu, Siyu Wang, Shanshan Zhou, Ming Kong, Qian Mao, Fang Long, Zhijun Fang, Song‐Lin Li

Bibliographic record

VenueFood & Function · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGinseng Biological Effects and Applications
Canadian institutionsThe Metabolomics Innovation Centre
FundersNational Natural Science Foundation of China
KeywordsGinsengChemotherapyMedicineAdverse effectLung cancerOncologyLungCancerPharmacologyTraditional medicineInternal medicineAlternative medicinePathology

Abstract

fetched live from OpenAlex

Chemotherapy is applied to treat non-small cell lung cancer (NSCLC), but often limited due to its unstable therapeutic effects and adverse reactions (ADRs). Ginseng and its main ingredients (ginsenosides and polysaccharides) have been clinically used as adjuvants to chemotherapy. However, their efficacies were based on individual trials with relatively small sample sizes, and it is difficult to draw a valid conclusion. In this study, eligible randomized controlled trials (RCTs) were searched in six international and Chinese databases (PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, Chinese VIP Information and Wanfang). The outcomes of the objective response rate (ORR), disease control rate (DCR), ADRs, quality of life (QOL), survival rates and immunity were extracted using standard data extraction forms. The efficacies of ginseng and its ingredients as adjuvants to chemotherapy in NSCLC were investigated and compared by meta-analysis and subgroup meta-analysis, respectively. A total of 28 RCTs including 2503 subjects were enrolled, and most of the eligible studies were of low-to-moderate quality. For the evaluation of ginseng and its ingredients as adjuvants to chemotherapy, the risk ratio (RR) or standardized mean difference (SMD) and 95% confidence intervals (CI) of the ORR, DCR, leucopenia, thrombocytopenia, myelosuppression, hepatotoxicity, nausea and vomiting, diarrhea, CD4+/CD8+ and one- and two-year survival rates, and QOL were 1.35 (1.21,1.50), 1.20 (1.14,1.28), 0.59 (0.50, 0.70), 0.53 (0.37, 0.76), 0.30 (0.17, 0.53), 0.67 (0.52, 0.87), 0.67 (0.53, 0.86), 0.42 (0.19, 0.96), 1.39 (0.63, 2.16), 1.35 (1.13, 1.60), 3.21 (1.51, 6.81) and 1.31 (1.22, 1.41) with significant differences. Subgroup analysis showed that ginseng enhanced nausea and vomiting and QOL, ginsenosides increased ORR, DCR, QOL, leucopenia, thrombocytopenia, myelosuppression, hepatotoxicity, diarrhea, CD4+/CD8+, and one- and two-year survival rates, while polysaccharides improved ORR, DCR, leucopenia, thrombocytopenia, myelosuppression, hepatotoxicity and nausea and vomiting during chemotherapy. In conclusion, ginseng and its ingredients facilitated the therapeutic effects of chemotherapy on NSCLC patients. Ginseng had beneficial effects on alleviating ADRs and enhancing QOL, ginsenosides demonstrated beneficial effects on enhancing therapeutic effects, reducing ADRs, improving immunity, prolonging survival rates and promoting QOL, while polysaccharides showed beneficial effects on promoting therapeutic effects and reducing ADRs.

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)0.008
metaresearch head score (Gemma)0.011
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 score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.016
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.311
Teacher spread0.290 · 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 designSystematic review
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

Citations42
Published2021
Admission routes1
Has abstractyes

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