MétaCan
Menu
Back to cohort
Record W2947538095 · doi:10.1186/s13045-019-0731-8

Emerging therapies for non-small cell lung cancer

2019· review· en· W2947538095 on OpenAlex
Chao Zhang, Natasha B. Leighl, Yi‐Long Wu, Wen‐Zhao Zhong

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Hematology & Oncology · 2019
Typereview
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsPrincess Margaret Cancer Centre
FundersNational Natural Science Foundation of China
KeywordsMedicineHematologyInternal medicineLung cancerOncologyIntensive care medicineCancerMedical physics

Abstract

fetched live from OpenAlex

Recent advances in the field of novel anticancer agents prolong patients' survival and show a promising future. Tyrosine kinase inhibitors and immunotherapy for lung cancer are the two major areas undergoing rapid development. Although increasing novel anticancer agents were innovated, how to translate and optimize these novel agents into clinical practice remains to be explored. Besides, toxicities and availability of these drugs in specific regions should also be considered during clinical determination. Herein, we summarize emerging agents including tyrosine kinase inhibitors, checkpoint inhibitors, and other potential immunotherapy such as chimeric antigen receptor T cell for non-small cell lung cancer attempting to provide insights and perspectives of the future in anticancer treatment.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.938
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.042
GPT teacher head0.450
Teacher spread0.407 · 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