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Record W3119630053 · doi:10.1002/1878-0261.12901

Circulating tumor cell profiling for precision oncology

2021· review· en· W3119630053 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMolecular Oncology · 2021
Typereview
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsUniversity of Toronto
FundersNational Cancer InstituteNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsCirculating tumor cellPrecision medicinePrecision oncologyMedicineProfiling (computer programming)Clinical OncologyBioinformaticsOncologyComputational biologyInternal medicineBiologyComputer sciencePathologyCancerMetastasis

Abstract

fetched live from OpenAlex

Analysis of circulating tumor cells (CTCs) collected from patient's blood offers a broad range of opportunities in the field of precision oncology. With new advances in profiling technology, it is now possible to demonstrate an association between the molecular profiles of CTCs and tumor response to therapy. In this Review, we discuss mechanisms of tumor resistance to therapy and their link to phenotypic and genotypic properties of CTCs. We summarize key technologies used to isolate and analyze CTCs and discuss recent clinical studies that examined CTCs for genomic and proteomic predictors of responsiveness to therapy. We also point out current limitations that still hamper the implementation of CTCs into clinical practice. We finally reflect on how these shortcomings can be addressed with the likely contribution of multiparametric approaches and advanced data analytics.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.088
GPT teacher head0.439
Teacher spread0.351 · 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