MétaCan
Menu
Back to cohort
Record W2154302986 · doi:10.5430/jst.v2n5p1

The place of TKI in the treatment of EGFR mutation-positive lung cancer

2012· article· en· W2154302986 on OpenAlexvenueno aff
Fumihiro Oshita, Shuji Murakami

Bibliographic record

VenueJournal of Solid Tumors · 2012
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsnot available
Fundersnot available
KeywordsGefitinibErlotinibLung cancerMedicineCarboplatinOncologyEpidermal growth factor receptorInternal medicineChemotherapyPaclitaxelCancer researchExonCancerBiologyGeneCisplatin

Abstract

fetched live from OpenAlex

About half of all Asian patients with non-small cell lung cancer (NSCLC) have tumors that are positive for epidermal growth factor receptor (EGFR) mutation, for which tyrosine kinase inhibitors (TKI) such as gefitinib or erlotinib are effective. Gefitinib has been shown to be a useful second-line treatment for NSCLC after platinum-based chemotherapy [ 1 ] , and some small-scale studies have also examined its activity as a first-line treatment for NSCLC, demonstrating a response rate of about 20% [ 2, 3 ] , which is similar to that of other anticancer drugs for NSCLC. However, gefitinib has failed to exert any additional effect when combined with platinum-based chemotherapy as a first-line treatment for NSCLC [ 4, 5 ] . On the other hand, two biological studies have demonstrated that gefitinib is effective in specifically targeting the EGFR gene with deletion in exon 19 or point mutation in exon 21, and tumor regression induced by gefitinib in NSCLC patients has been shown to be correlated with the presence of these mutations in lung tumors [ 6, 7 ] . A Japanese study has demonstrated that patients with postoperative recurrence of EGFR mutation-positive NSCLC showed a good tumor response to gefitinib and achieved longer survival than patients whose tumors lacked EGFR mutation [ 8 ] . A study designed to compare carboplatin plus paclitaxel with gefitinib for chemo-naïve Asian patients with both EGFR mutation-positive and -negative NSCLC demonstrated that patients with EGFR mutation-positive tumors achieved significantly longer overall and progression-free survival with gefitinib than with carboplatin plus paclitaxel in subset analysis [ 9 ] . Thereafter, two large studies designed to compare platinum-based chemotherapy with gefitinib therapy for chemo-naïve NSCLC patients with EGFR mutation were performed in Japan. In both studies, the progression-free survival achieved with gefitinib was about twice as long as that achieved with standard platinum-based chemotherapy [ 10, 11 ] . These data indicated that gefitinib is an effective first-line chemotherapy for NSCLC harboring EGFR mutation.

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 score0.210
Threshold uncertainty score0.133

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 head0.012
GPT teacher head0.364
Teacher spread0.351 · 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
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Solid TumorsSame topicLung Cancer Treatments and MutationsFrench-language works237,207