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Record W4411992242 · doi:10.1016/j.jtocrr.2025.100876

Characteristics of Short-Term Survivors With ALK or ROS1-Altered Metastatic NSCLC

2025· article· en· W4411992242 on OpenAlexaff
Kai Liu, Connor B. Grady, Geoffrey Liu, Devalben Patel, Karmugi Balaratnam, Stephen V. Liu, Gabriela Bravo, Yunan Nie, Jorgé Nieva, Amanda Herrmann, Kristen A. Marrone, Vincent K. Lam, Fangdi Sun, Jonathan E. Dowell, William Schwartzman, Vamsidhar Velcheti, Olivia Fankuchen, Tasfiq Ullah, L. Villaruz, Matthew K. Nguyen, Jared Weiss, Shetal Patel, Kelsey Miller, Wade T. Iams, Krishna Chandrasekhara, William Tompkins, Tejas Patil, Dara L. Aisner, D. Ross Camidge, Wei‐Ting Hwang, Lova Sun, Melina E. Marmarelis

Bibliographic record

VenueJTO Clinical and Research Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsPrincess Margaret Cancer Centre
FundersTakeda OncologyTakeda Pharmaceutical CompanyLUNGevity Foundation
KeywordsMedicineLung cancerOncologyROS1Internal medicineTerm (time)CancerAdenocarcinoma

Abstract

fetched live from OpenAlex

Introduction: + metastatic NSCLC (mNSCLC) remains unclear. As we consider intensification strategies, it is critical to identify factors that predict high-risk disease. Methods: + mNSCLC. Baseline characteristics and the cumulative incidence (CI) of brain and liver metastases were compared (≥2-year survivors versus <2-year; pre-2017 versus post-2017). Multivariable Cox proportional hazard models were used to evaluate the association between factors and overall survival, and multivariable logistic regression models were used for the odds of death within 2 years. Results: = 0.201). Conclusions: + mNSCLC, the presence of liver metastases at baseline and on-treatment was associated with worse survival. In the ALK+ population, the cumulative incidence of brain but not liver metastases is improving, highlighting a need for therapies effective at the treatment and prevention of liver metastases.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.054
Threshold uncertainty score0.226

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.138
GPT teacher head0.520
Teacher spread0.382 · 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
Published2025
Admission routes1
Has abstractyes

Explore more

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