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Record W4403537638 · doi:10.1016/j.esmoop.2024.103754

8P Precision medicine drug testing platform to guide the treatment of EML4-ALK fusion lung cancers

2024· article· en· W4403537638 on OpenAlexaff
Sofía D. Merajver, Aaron M. Udager, Angel Qin, Lan Bao, Xu Cheng, Hamadi Madhi, L. Goo, Varun Kathawate, Peter Ulintz, Albert Liu, Habib Serhan, Vishal Navani, John L. Jefferies, M. Sanni Ali, Michael J. Monument, Johannes R. Kratz, Amber Smith, Matthew B. Soellner, Nathan Merrill

Bibliographic record

VenueESMO Open · 2024
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicinePrecision medicineDrugLungOncologyMedical physicsInternal medicinePharmacologyPathology

Abstract

fetched live from OpenAlex

Lung cancer (LC) remains the top cause of cancer-associated mortality worldwide, with a 10-year overall survival rate of only 5%. While most LCs are smoking related, 25% of non-small cell LC (NSCLC) are diagnosed in persons with little or no smoking history. Fusions involving anaplastic lymphoma kinase (ALK) are the oncogenic driver in ∼3–7% of NSCLC. While inhibitors targeting the kinase domain of ALK (TKIs) have proven effective, inevitably, resistance develops with limited effective treatment options.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score0.976

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 head0.047
GPT teacher head0.413
Teacher spread0.366 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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