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Record W4411165957 · doi:10.1016/j.jlb.2025.100304

Cancer in a Drop: Liquid biopsy insights from AACR 2025

2025· editorial· en· W4411165957 on OpenAlexaff
Shivahamy Maheswaran, Roberto Borea, Diego de Miguel‐Pérez, Pasquale Pisapia, Nadia Ghazali, Canio Martinelli, Pavel Stejskal, Carolina Reduzzi

Bibliographic record

VenueThe Journal of Liquid Biopsy · 2025
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsCancerBiopsyMedicineInternal medicine

Abstract

fetched live from OpenAlex

The American Association of Cancer Research (AACR) Annual Meeting is an internationally renowned conference that consolidates and reports the latest breakthroughs in basic, translational, clinical, and epidemiological sciences. This editorial, written on behalf of the International Society of Liquid Biopsy (ISLB) Young Committee, highlights the most recent research efforts presented at the AACR Annual Meeting 2025, focusing on liquid biopsy (LB) methods and applications. As our understanding of LB continues to expand, its role along the cancer care continuum becomes increasingly apparent.

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)0.009
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.012
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.022
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.001
Science and technology studies0.0020.002
Scholarly communication0.0070.004
Open science0.0020.001
Research integrity0.0090.015
Insufficient payload (model declined to judge)0.0120.010

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 head0.267
Teacher spread0.261 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations1
Published2025
Admission routes1
Has abstractyes

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