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Record W7112335456

Systemic therapies in metastatic non-small-cell lung cancer with emphasis on targeted therapies: the rational approach

2010· article· en· W7112335456 on OpenAlexaboutno aff

Bibliographic record

VenuePubMed Central · 2010
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsnot available
Fundersnot available
KeywordsErlotinibBevacizumabTargeted therapyLung cancerMaintenance therapyRegimenDiseaseChemotherapy
DOInot available

Abstract

fetched live from OpenAlex

Historically, first-line treatment of non-small-cell lung cancer (nsclc) has been based on giving a limited number of cycles of chemotherapy to achieve tumour response or stable disease. Patients are then observed without active therapy until disease progresses, at which point, subsequent lines of therapy are given. In recent years, two new concepts have been introduced to the management of nsclc: maintenance therapy and therapy with targeted agents. Maintenance therapy—with either a chemotherapeutic or biologic agent—is given immediately after first-line therapy to patients who have achieved tumour response or stable disease. Choice of therapy may include continuation of the agents included in the induction regimen or introduction of different agents (early second-line treatment) with the aim of preventing progression and prolonging progression-free survival. Targeted agents such as bevacizumab and erlotinib target critical molecular signalling pathways and provide several advantages over chemotherapy, including fewer toxicities and the possibility of a longer duration of therapy. This review examines the treatment options in all lines of therapy for metastatic nsclc, focusing particularly on targeted therapies that have been approved in the United States, Canada, or Europe.

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 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.408

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 head0.013
GPT teacher head0.264
Teacher spread0.251 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2010
Admission routes1
Has abstractyes

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