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Record W1980138637 · doi:10.3747/co.v16i1.406

Targeting mtor-Dependent Tumours with Specific Inhibitors: A Model for Personalized Medicine based on Molecular Diagnoses

2009· article· en· W1980138637 on OpenAlexaffvenue
Luc Furic, Mark Livingstone, Ryan J.O. Dowling, Nahum Sonenberg

Bibliographic record

VenueCurrent Oncology · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsMcGill University
Fundersnot available
KeywordsPI3K/AKT/mTOR pathwayMedicinePersonalized medicineCancer researchIntracellularPrecision medicineCancerDiscovery and development of mTOR inhibitorsBioinformaticsComputational biologySignal transductionBiologyCell biologyInternal medicinePathology

Abstract

fetched live from OpenAlex

Cancer cells are characterized by aberrant growth arising from deregulated signalling pathways. The mammalian target of rapamycin (mTOR) pathway integrates multiple growth signals coming from both intracellular and extracellular cues. In this short review, we summarize what is known about the efficacy of targeting the mTOR pathway to treat cancer patients, and we explain the rationale behind promising new inhibitors that could show more potent tumour growth inhibition than did the first generation of these drugs.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.052
GPT teacher head0.357
Teacher spread0.304 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations16
Published2009
Admission routes2
Has abstractyes

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