MétaCan
Menu
Back to cohort
Record W2890392090 · doi:10.1186/s12943-018-0888-8

CircRNA microarray profiling identifies a novel circulating biomarker for detection of gastric cancer

2018· letter· en· W2890392090 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMolecular Cancer · 2018
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCircular RNAs in diseases
Canadian institutionsnot available
FundersNatural Science Foundation of Jiangsu ProvinceGovernment of Jiangsu ProvinceNanjing Medical UniversityMcMaster University
KeywordsBiomarkerMicrovesiclesBiologyCancerClinical significanceMetastasismicroRNACancer researchInternal medicineMedicineGene

Abstract

fetched live from OpenAlex

CircRNA expression profiles for gastric cancer (GC) were screened using plasma samples from 10 GC patients with different TNM stages and 5 healthy individuals as controls. Results showed lower expression of circ-KIAA1244 in GC tissues, plasmas, and cells compare to normal controls. Further clinical data analysis demonstrated that a decreased expression of circ-KIAA1244 in plasmas was negatively correlated with TNM stage and lymphatic metastasis, and a shorter overall survival time of GC patients. Moreover, we found that circ-KIAA1244 could be detected in GC plasma exosomes and showed no obvious significance compared to the expression level in the corresponding plasmas. This study revealed a GC-tissues-derived circ-KIAA1244 could serve a novel circulating biomarker for detection of GC.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.023
GPT teacher head0.291
Teacher spread0.268 · 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