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Record W2096982880 · doi:10.1093/jncimonographs/lgr023

Tissue and Soluble Biomarkers in Breast Cancer and Their Applications: Ready to Use?

2011· article· en· W2096982880 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJNCI Monographs · 2011
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsUniversity Hospital Foundation
Fundersnot available
KeywordsMedicineBreast cancerCancerIntensive care medicineBioinformaticsOncologyInternal medicine

Abstract

fetched live from OpenAlex

Breast cancer therapies are in continuous evolution: From surgery to hormonal therapy, from classical and new combined chemotherapies to emerging targeted agents of recent introduction to the clinic. The attempt to personalize the best treatment for each patient is driven by efficacy and safety parameters and tumor biology investigations of markers for aggressiveness and response to treatment. The plethora of targeted therapies has provided momentum for the quest to better understand not only target mechanisms of action, but also tumor behavior. Moreover, how to monitor response to these agents is crucial today to achieve better resource-sharing and to find cheaper, less invasive, and standardized detection techniques for clinically validated biomarkers. In this report, we briefly summarize data on the major tissue and soluble biomarkers focusing on their actual use in daily practice, as well as their emerging role and possible future applications in breast cancer treatment.

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.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.041
GPT teacher head0.285
Teacher spread0.244 · 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