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Record W4320914516 · doi:10.6004/jnccn.2022.7089

Small Cell Lung Cancer in Light/Never Smokers – A Role for Molecular Testing?

2023· article· en· W4320914516 on OpenAlexaff
Gordon Taylor Moffat, Tao Wang, Andrew Robinson

Bibliographic record

VenueJournal of the National Comprehensive Cancer Network · 2023
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Research Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineLung cancerChemotherapyOncologyDiseaseCancerInternal medicineIncidence (geometry)Cancer research

Abstract

fetched live from OpenAlex

This report describes the management of small cell lung cancer (SCLC) transformation in a patient with untreated ALK-mutated advanced disease and a minimal smoking history, and a separate case of a de novo SCLC in a lifelong nonsmoker found to have a potentially targetable ERBB2 alteration. In the first case, chemotherapy followed by a targeted inhibitor was chosen due to the presence of the ALK rearrangement, as well as a somewhat discordant response to induction chemotherapy, suggesting possible progression of the ALK inhibitor-sensitive component. Molecular testing for the identification of driver mutations should be considered in patients with SCLC who have light/never smoking histories in order to help understand the incidence and ultimate optimal management strategies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.062
GPT teacher head0.370
Teacher spread0.308 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2023
Admission routes1
Has abstractyes

Explore more

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