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Record W4404580874 · doi:10.1016/j.jlb.2024.100224

PAN-CANCER ANALYSIS OF PRE-TREATMENT CIRCULATING TUMOR DNA (CTDNA) IN PATIENTS FROM THE PRINCESS MARGARET LIQUID BIOPSY PROGRAM

2024· article· en· W4404580874 on OpenAlexaff
Scott Strum, Clodagh Murray, Sofia Genta, Enrique Sanz‐García, Albiruni R. Abdul Razak, Stéphanie Lheureux, Christodoulos Pipinikas, Christopher G. Smith, Mitchell J. Elliott, David W. Cescon

Bibliographic record

VenueThe Journal of Liquid Biopsy · 2024
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsKingston Health Sciences CentrePrincess Margaret Cancer Centre
Fundersnot available
KeywordsLiquid biopsyCirculating tumor DNACancerDNAMedicineInternal medicineOncologyBiopsyBiologyGenetics

Abstract

fetched live from OpenAlex

Introduction: ctDNA has significant potential as a tool in cancer management, including detection of residual disease (dx) and treatment (Tx) monitoring. Despite extensive research, the landscape of ctDNA detection across various dx settings remains poorly understood. Here we explore data generated using the same high-sensitivity assay from multiple tumor types.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.320
Teacher spread0.306 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2024
Admission routes1
Has abstractyes

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