MétaCan
Menu
Back to cohort
Record W3192563092 · doi:10.1016/j.lungcan.2021.08.003

Canadian ROS proto-oncogene 1 study (CROS) for multi-institutional implementation of ROS1 testing in non-small cell lung cancer

2021· article· en· W3192563092 on OpenAlexafffundabout
Carol C. Cheung, Adam C. Smith, Roula Albadine, Gilbert Bigras, Anna Bojarski, Christian Couture, Jean‐Claude Cutz, Weei‐Yuan Huang, Diana N. Ionescu, Doha Itani, Iyare Izevbaye, Aly Karsan, Margaret M. Kelly, Joan H.M. Knoll, Keith Kwan, Michel R. Nasr, Gefei Qing, Fariboz Rashid-Kolvear, Harmanjatinder S. Sekhon, Alan Spatz, Tracy Stockley, Danh Tran‐Thanh, Tracy Tucker, Ranjit Waghray, Hangjun Wang, Zhaolin Xu, Yasushi Yatabe, Emina Torlakovic, Ming‐Sound Tsao

Bibliographic record

VenueLung Cancer · 2021
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsSaskatchewan Health AuthorityUniversity of SaskatchewanDalhousie UniversityQueen Elizabeth II Health Sciences CentreMcGill University Health CentreUniversity of OttawaCalgary Laboratory ServicesUniversity of ManitobaUniversity of CalgarySpinal Cord Injury BCCanada's Michael Smith Genome Sciences CentreSunnybrook Health Science CentreOttawa HospitalUniversity Health NetworkMcMaster University Medical CentreHealth Sciences CentreLondon Health Sciences CentreHealth Sciences NorthInstitut Universitaire de Cardiologie et de Pneumologie de QuébecCentre Hospitalier de l’Université de MontréalUniversité LavalUniversity of AlbertaUniversity of Toronto
FundersPfizer Canada
KeywordsROS1ImmunohistochemistryMedicineCrizotinibLung cancerFluorescence in situ hybridizationCancer researchPathologyCancerOncogeneAdenocarcinomaInternal medicineBiologyGeneCell cycleGenetics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.380
Threshold uncertainty score0.999

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 head0.036
GPT teacher head0.406
Teacher spread0.370 · 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

Citations22
Published2021
Admission routes3
Has abstractno

Explore more

Same venueLung CancerSame topicLung Cancer Treatments and MutationsFrench-language works237,207