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Record W2967404030 · doi:10.1186/s12916-019-1400-z

New approaches for detecting cancer with circulating cell-free DNA

2019· letter· en· W2967404030 on OpenAlexaff
Clare Fiala, Eleftherios P. Diamandis

Bibliographic record

VenueBMC Medicine · 2019
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsSinai Health SystemMount Sinai Hospital
Fundersnot available
KeywordsMedicineCell-free fetal DNACirculating tumor DNABiomarkerCancerCancer detectionLiquid biopsyDNAOncologyInternal medicineComputational biologyCancer researchBioinformaticsGeneticsBiology

Abstract

fetched live from OpenAlex

Circulating cell-free DNA (cfDNA), released from normal and cancerous cells, is an exciting new biomarker. Circulating tumor DNA (ctDNA) usually contains genetic changes that could be useful for detecting cancer. Various laboratories have reported impressive clinical data, with cancer detection sensitivities ranging from 50 to 70%, calculated at 90–95% specificities [1–3]. ctDNA has additional important applications, including prognosis, monitoring therapy and estimating tumor volume [4]. These applications are less controversial than early cancer detection, and are used widely in research settings. Despite promising reports [1–3], there are some major concerns when using ctDNA for early diagnosis [5, 6].

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 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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.306
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.041
GPT teacher head0.252
Teacher spread0.211 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations32
Published2019
Admission routes1
Has abstractyes

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