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Association of liquid biopsy and gastric cancer patients' prognosis: Comprehensive synopsis and a meta-analysis.

2018· article· en· W2789908368 on OpenAlexaboutno aff
Lin Chen, Yunhe Gao, Hongqing Xi, Jianxin Cui, Aizhen Cai, Weisong Shen, Kecheng Zhang, Jiyang Li, Bo Wei

Bibliographic record

VenueJournal of Clinical Oncology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLiquid biopsyInternal medicineHazard ratioMeta-analysisCirculating tumor cellCancerConfidence intervalOncologySubgroup analysisBiopsyGastroenterologyMetastasis

Abstract

fetched live from OpenAlex

25 Background: Liquid biopsy including circulating tumor cells (CTCs) and cell-free nucleic acids (cfNAs) has offered a minimally invasive approach for detection and measurement of gastric cancer (GC). Reports regarding associations between liquid biopsy and gastric cancer have been emerging rapidly in recent decades, yet their prognostic value still remains paradoxical. Methods: We searched Medline, Embase, Cochrane Central Register of Controlled Trials database for relevant studies that assessed the prognosis significance of CTCs and cfNAs in gastric cancer from peripheral blood(PB). Newcastle-Ottawa Scale (NOS) was used to assess the quality of evidence. Stata 12.0 was used for pooled analysis and subgroup analysis was performed to determine the association of CTCs or cfNAs’ presence and major clinical characteristics. Pooled results were displayed as hazard ratios (HRs) with their 95% confidence intervals (95% CI) with random effect models. Results: We identified 1258 studies, and then 43 were finally eligible for analysis. A total of 3792 patients were included for final evaluation. Pooled analysis showed that detection of certain CTCs, ctDNA or circulating miRNA was associated with poorer overall survival (OS) (CTCs, HR=2.05, 95%CI 1.65-2.55, p < 0.001; circulating miRNA HR=1.74, 95%CI 1.13-2.69, p=0.013; ctDNA, HR=1.77, 95%CI 1.28-2.44, p=0.001) and disease-free survival(DFS) (CTCs, HR=2.92, 95%CI 1.93-4.40, p < 0.001; circulating miRNA, HR=3.30, 95%CI 2.39-4.55, p < 0.001; ctDNA, HR=4.69, 95%CI 2.23-9.86, p < 0.001) of gastric cancer patients, regardless of the disease’s early or late stage. Conclusions: Several high-quality circulating biomarkers or detection methods for gastric cancer prognosis prediction were identified by subgroup analysis, including the Cellsearch system, cytokeratins, miR-20a, miR-200c, etc. Detection of these certain dysregulation circulating markers in patients’ PB indicates poor prognosis with advanced GC patients.

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)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.019
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0140.042
Bibliometrics0.0080.010
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.002
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 head0.073
GPT teacher head0.405
Teacher spread0.332 · 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 designMeta-analysis
Domainnot available
GenreEmpirical

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".

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Citations1
Published2018
Admission routes1
Has abstractyes

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