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Record W1983651288 · doi:10.3747/co.19.1132

Personalized medicine in metastatic non-small-cell lung cancer: promising targets and current clinical trials

2012· article· en· W1983651288 on OpenAlexaffvenue
A. Black, Donald G. Morris

Bibliographic record

VenueCurrent Oncology · 2012
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineLung cancerClinical trialOncologyDiseasePrecision medicinePersonalized medicineInternal medicineQuality of life (healthcare)CancerStage (stratigraphy)Targeted therapyChemotherapyIntensive care medicineBioinformaticsPathology

Abstract

fetched live from OpenAlex

Non-small-cell lung cancer (nsclc) remains the leading cause of cancer-related death globally, with most patients presenting with non-curable disease. Platinum-based doublet chemotherapy has been the cornerstone of treatment for patients with advanced-stage disease and has resulted in a modest increase in overall survival (on the order of an incremental 2 months increased survival per decade) and quality of life. Improved knowledge of the molecular signalling pathways found in nsclc has led to the development of biomarkers with associated targeted therapeutics, thus changing the treatment paradigm for many nsclc patients. In this review, we present a summary of many of the currently investigated nsclc targets, discuss their current clinical trial status, and provide commentary as to the likelihood of their success making a positive impact for nsclc patients.

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)0.011
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.267
GPT teacher head0.573
Teacher spread0.306 · 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
GenreReview

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

Citations12
Published2012
Admission routes2
Has abstractyes

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