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Record W4224238459 · doi:10.1126/sciadv.abi8618

Monitoring and adapting cancer treatment using circulating tumor DNA kinetics: Current research, opportunities, and challenges

2022· review· en· W4224238459 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.

Bibliographic record

VenueScience Advances · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsCirculating tumor DNACancerAdaptation (eye)Precision medicineMedicineBiomarkerCancer treatmentComputational biologyClinical PracticeKineticsOncologyBioinformaticsCancer researchBiologyInternal medicinePathologyGeneticsNeuroscience

Abstract

fetched live from OpenAlex

Circulating tumor DNA (ctDNA) has emerged as a biomarker with wide-ranging applications in cancer management. While its role in guiding precision medicine in certain tumors via noninvasive detection of susceptibility and resistance alterations is now well established, recent evidence has pointed to more generalizable use in treatment monitoring. Quantitative changes in ctDNA levels over time (i.e., ctDNA kinetics) have shown potential as an early indicator of therapeutic efficacy and could enable treatment adaptation. However, ctDNA kinetics are complex and heterogeneous, affected by tumor biology, host physiology, and treatment factors. This review outlines the current preclinical and clinical knowledge of ctDNA kinetics in cancer and how early on-treatment changes in ctDNA levels could be applied in clinical research to collect evidence to support implementation in daily practice.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.424
GPT teacher head0.461
Teacher spread0.037 · 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