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Can circulating tumor DNA be used for direct and early stage cancer detection?

2017· preprint· en· W2774583854 on OpenAlex
Eleftherios P. Diamandis, Clare Fiala

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

VenueF1000Research · 2017
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity of TorontoUniversity Health NetworkMount Sinai Hospital
FundersCanadian Cancer Society
KeywordsOpen peer reviewPlant biologyStage (stratigraphy)Circulating tumor DNABiologyPhysiologyCancerMedicineNeuroscienceComputational biologyInternal medicineBotany

Abstract

fetched live from OpenAlex

In the August 16th issue of Science Translational Medicine, Phallen et al propose a method for early cancer diagnosis by using circulating tumor DNA (1). One major advance of this paper includes optimized sequencing of cell-free/circulating tumor DNA (ctDNA) without knowledge of tumor mutations. Evaluation of 200 patients with colorectal, breast, lung and ovarian cancer revealed mutations in ctDNA in approx. 60-70% of all patients, including stage 1 and stage 2 disease. If this data can be reproduced in asymptomatic individuals, they will likely have a major impact on early cancer detection and patient outcomes. In this commentary, we examine the feasibility of this approach for detecting small, asymptomatic tumors, based on previously published empirical data.

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)0.001
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 score0.193
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.001
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 head0.049
GPT teacher head0.360
Teacher spread0.312 · 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