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Record W4404581182 · doi:10.1016/j.jlb.2024.100189

Detection of CTDNA after neoadjuvant chemotherapy predicts distant relapse-free survival, local and distant recurrence in TNBC: Findings from Tricia study

2024· article· en· W4404581182 on OpenAlexaff
Talia Roseshter, Anna Klemantovich, Josiane Lafleur, Cathy Lan, Luca Cavallone, O Elebute, Jean-François Boileau, Manuela Pelmus, Rossanna C. Pezo, Muriel Brackstone, Terry L. Ng, Adriana Aguilar, Mark Basik

Bibliographic record

VenueThe Journal of Liquid Biopsy · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsOttawa HospitalSunnybrook HospitalJewish General HospitalLondon Health Sciences Centre
FundersInstituto de Salud Carlos IIICentro de Investigación Biomédica en Red de Cáncer
KeywordsOncologyMedicineChemotherapyNeoadjuvant therapyInternal medicineCancerBreast cancer

Abstract

fetched live from OpenAlex

Introduction: Triple negative breast cancer (TNBC) patients who have a residual tumor at surgery (non-pCR) following neoadjuvant chemotherapy (NAC) have a very poor prognosis. Adjuvant capecitabine improves relapse-free survival (RFS) by 15%. There is a need for biomarkers to identify patients who may not require adjuvant capecitabine. The TRICIA trial (NCT04874064) accrued non-pCR TNBC patients for ctDNA measurements at pre-operative (T1), post-operative (T2), 3-month (T3) and 6-month (T4) time points using hospital-based tumor-specific personalized assays.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.007
GPT teacher head0.240
Teacher spread0.233 · 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 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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