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Optimal Adjuvant Therapy for Non-Small Cell Lung Cancer—How to Handle Stage I Disease

2007· review· en· W2108979762 on OpenAlexaboutno aff
Heather A. Wakelee, Sarita Dubey, David R. Gandara

Bibliographic record

VenueThe Oncologist · 2007
Typereview
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLung cancerStage (stratigraphy)AdjuvantAdjuvant therapyOncologyDiseaseChemotherapyAdjuvant chemotherapyInternal medicineCancerIntensive care medicineBreast cancer

Abstract

fetched live from OpenAlex

The standard of care for resected stage II-IIIA non-small cell lung cancer (NSCLC) now includes adjuvant chemotherapy based on the results of three phase III studies using cisplatin-based regimens--the International Adjuvant Lung Trial, the National Cancer Institute of Canada JBR.10 trial, and the Adjuvant Navelbine International Trialist Association trial. The role of adjuvant chemotherapy for stage I disease remains controversial. A recent meta-analysis (the Lung Adjuvant Cisplatin Evaluation) showed potential harm with the addition of adjuvant cisplatin for stage IA disease and no survival benefit for this modality in stage IB disease. Updated results from the Cancer and Leukemia Group B 9633 trial, the only trial to focus exclusively on stage IB patients, no longer show a statistically significant survival benefit from adjuvant chemotherapy in this population, except for the subgroup of patients with larger tumors. It may be that trials have been underpowered to detect a small benefit for patients with stage IB disease, or there may really not be benefit to adding adjuvant therapy for this stage of disease. Additional markers, such as tumor size or the presence or absence of certain tumor proteins like ERCC1, may help to determine which patients with resected stage I NSCLC may benefit from adjuvant chemotherapy. Strategies such as inhibition of angiogenesis pathways and the epidermal growth factor receptor are under exploration.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.929
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
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 head0.086
GPT teacher head0.418
Teacher spread0.332 · 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.

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

Citations26
Published2007
Admission routes1
Has abstractyes

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