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Record W2427710323 · doi:10.3747/co.23.2920

Astragalus-Containing Traditional Chinese Medicine, with and without Prescription Based on Syndrome Differentiation, Combined with Chemotherapy for Advanced Non-Small-Cell Lung Cancer: A Systemic Review and Meta-Analysis

2016· review· en· W2427710323 on OpenAlexvenueno aff
Shuang Wang, Q. Wang, Lijing Jiao, Yiwei Huang, D. Garfield, Jialong Zhang, Ling Xu

Bibliographic record

VenueCurrent Oncology · 2016
Typereview
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsnot available
FundersFok Ying Tong Education FoundationScience and Technology Commission of Shanghai Municipality
KeywordsMedicineLung cancerInternal medicineHazard ratioMeta-analysisOncologyTraditional Chinese medicineMedical prescriptionAdverse effectChemotherapyTraditional medicineCancerConfidence intervalAlternative medicinePharmacologyPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: Traditional Chinese Medicine (tcm) is used in China as part of the treatment for non-small-cell lung cancer (nsclc) and often includes prescription of herbal therapy based on syndrome differentiation. Studies of various Astragalus-based Chinese medicines combined with platinum-based chemotherapy in the treatment of lung cancer are popular in East Asia, particularly in China. The aim of the present study was to perform a systematic review and meta-analysis comparing platinum-based chemotherapy alone with platinum-based chemotherapy plus Astragalus-based Chinese botanicals, with and without prescription based on syndrome differentiation, as first-line treatment for advanced nsclc. METHODS: We searched the Chinese Biomedical Literature database, the China National Knowledge Internet, the VIP Chinese Science and Technology Periodicals Database, PubMed, embase, the Cochrane databases, and abstracts presented at meetings of the American Society of Clinical Oncology, the World Conference on Lung Cancer, the European Society for Medical Oncology, and the Chinese Society of Clinical Oncology for all eligible studies. Endpoints were overall survival; 1-year, 2-year, and 3-year survival rates; performance status; overall response rate; and grade 3 or 4 adverse events. Subgroup analyses based on herbal formulae individualized using syndrome differentiation or on oral or injection patent medicines were performed using the Stata software application (version 11.0: StataCorp LP, College Station, TX, U.S.A.) and a fixed-effects or random-effects model in case of heterogeneity. Results are expressed as a hazard ratio (hr) or relative risk (rr), with corresponding 95% confidence intervals (cis). RESULTS: Seventeen randomized studies with scores on the Jadad quality scale of 2 or more, representing 1552 patients, met the inclusion criteria. Compared with platinum-based chemotherapy alone, the addition of Astragalus-based tcm to chemotherapy was associated with significantly increased overall survival (hr: 0.61; 95% ci: 0.42 to 0.89; p = 0.011); 1-year (rr: 0.73; 95% ci: 0.65 to 0.82; p < 0.001), 2-year (rr: 0.3344; 95% ci: 0.237 to 0.4773; p < 0.001), and 3-year survival rates (rr: 0.30; 95% ci: 0.17 to 0.53; p < 0.001); performance status (rr: 0.43; 95% ci: 0.34 to 0.55; p < 0.001); and tumour overall response rate (rr: 0.7982; 95% ci: 0.715 to 0.89; p < 0.001). Subgroup analyses indicated that Astragalus herbal formulae given based on syndrome differentiation were more effective than Astragalus-based oral and injection patent medicines. Side effects-including anemia, neutropenia, thrombocytopenia, fatigue, poor appetite, nausea, and vomiting-were significantly more frequent with platinum-based chemotherapy alone than when platinum-based chemotherapy was combined with Astragalus-based tcm. CONCLUSIONS: Astragalus-based Chinese botanical therapy, especially when based on syndrome differentiation, is associated with increased efficacy of platinum-based chemotherapy and decreased platinum-derived toxicities for patients with advanced nsclc.

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.622
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0000.000
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 head0.082
GPT teacher head0.423
Teacher spread0.341 · 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 teacher head, not a consensus.

Study designMeta-analysis
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

Citations51
Published2016
Admission routes1
Has abstractyes

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