MétaCan
Menu
Back to cohort
Record W3008822475 · doi:10.2217/lmt-2019-0017

Optimizing Molecular Residual Disease Detection using Liquid Biopsy Postoperatively in Early Stage Lung Cancer

2020· editorial· en· W3008822475 on OpenAlexaff
Anna McGuire, Curtis Hughesman, Melissa K. McConechy, B. Melosky, Stephen Lam, Renelle Myers, John Yee, Ernest Tang, Stephen Yip

Bibliographic record

VenueLung Cancer Management · 2020
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsBC Cancer AgencyVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsStage (stratigraphy)MedicineDiseaseLung cancerResidualLiquid biopsyLungBiopsyCancerRadiologyOncologyPathologyInternal medicineBiologyComputer science

Abstract

fetched live from OpenAlex

Stage I/II NSCLC surgical patients with MRD undergoing liquid biopsy postoperatively would be the ideal target population for assessment of the impact of adjuvant targeted systemic therapy on disease-free survival."

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)0.007
metaresearch head score (Gemma)0.016
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 score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.001
Research integrity0.0110.021
Insufficient payload (model declined to judge)0.0040.005

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 head0.285
Teacher spread0.278 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueLung Cancer ManagementSame topicCancer Genomics and DiagnosticsFrench-language works237,207