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Circulating Tumor Cells: A Window to Understand Cancer Metastasis, Monitor and Fight Against Cancers

2015· article· en· W2070298043 on OpenAlex
Lei Xu, Jonathan Shamash, Yong‐Jie Lu

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.

venuePublished in a venue whose home country is Canada.
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

VenueJournal of cancer research updates · 2015
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsnot available
Fundersnot available
KeywordsCirculating tumor cellMetastasisCancerMalignancyCancer metastasisMedicineCancer researchOncologyComputational biologyBiologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

Metastases are the major culprits behind most cancer-related death and the central challenge to the eradication of a malignancy. Circulating tumor cells (CTCs) have the potential to help us understand how metastases form, to be utilized for cancer diagnosis and treatment selection and even to be targeted for cancer treatment. Many advances have been made regarding the isolation of these rare cells. However, several challenges and limitations in CTC analysis still exist. Multiple color immunofluorescence, genetic analysis (e.g. Fluorescence in situ Hybridization, microarray and next generation sequencing) and CTC culture will be effective tools to study CTCs and provide information on metastatic mechanism and clinical implication. In this review, we discuss the importance of CTC study in understanding cancer metastasis and their potential clinical application as biomarkers to predict cancer progression and treatment response, as well as the current situation for CTC isolation and analysis.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.754

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.146
GPT teacher head0.440
Teacher spread0.294 · 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