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Record W2563856179 · doi:10.17140/prrmoj-2-116

Comparison of Next-Generation Sequencing Platforms for Clinical Testing of Non-Small Cell Lung Cancer

2015· article· en· W2563856179 on OpenAlexafffund
Rama Kant Singh, Susanne Penny, Susan E. Douglas

Bibliographic record

VenuePulmonary Research and Respiratory Medicine - Open Journal · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsNational Research Council Canada
FundersDalhousie UniversityMcGill UniversityUniversity of Texas Southwestern Medical CenterMassachusetts General Hospital
KeywordsIon semiconductor sequencingDNA sequencingComputational biologyAmpliconLung cancerKRASMultiplexMedicineOncologyCancerBioinformaticsBiologyInternal medicineGeneGeneticsPolymerase chain reaction

Abstract

fetched live from OpenAlex

Personalized treatment of lung cancer using therapies that target activating oncogenic mutations such as EGFR and ALK has become the standard of care. Current molecular testing is routinely performed for single genes and increasingly in a multiplex format. However, the scarcity of sufficient biopsy material has necessitated a more high-throughput and comprehensive testing approach. Next Generation Sequencing (NGS) offers great promise as a highly sensitive method of detection for a variety of biopsy sources (tissue, blood, pleural effusions). However, there are multiple NGS platforms and panels with varying advantages and disadvantages.

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)0.004
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 head0.526
GPT teacher head0.507
Teacher spread0.019 · 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 designBench or experimental
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

Citations1
Published2015
Admission routes2
Has abstractyes

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