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Record W4404581053 · doi:10.1016/j.jlb.2024.100204

An mDetect assay for monitoring treatment response in metastatic breast cancer patients

2024· article· en· W4404581053 on OpenAlexaff
Keira Frosst, Brooke Wilson, Katrina Cristall, Catherine Crawford-Brown, Christopher R. Mueller

Bibliographic record

VenueThe Journal of Liquid Biopsy · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Biosensing Techniques and Applications
Canadian institutionsQueen's University
Fundersnot available
KeywordsMetastatic breast cancerBreast cancerOncologyMedicineInternal medicineCancer

Abstract

fetched live from OpenAlex

Metastatic breast cancer treatment response is currently assessed every 3-6 months and this long delay results in many patients undergoing ineffective and toxic treatments for long periods of time. We have developed the methylation DETEction of Circulating Tumour DNA (mDETECT) assay, a targeted DNA methylation-based Next Generation Sequencing liquid biopsy that detects cancer-specific patterns in all subtypes of breast cancer. The assay targets 60 differentially hypermethylated regions in breast cancer across the genome and assesses over 400 individual CpGs with an average of 8 CpGs per target amplicon.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.344
Teacher spread0.326 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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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