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Record W3024486891 · doi:10.21037/jtd-19-4119

Bronchoscopic tissue yield for advanced molecular testing: are we getting enough?

2020· review· en· W3024486891 on OpenAlexafffund
Pattraporn Tajarernmuang, Linda Ofiara, Stéphane Beaudoin, Anne V. Gonzalez

Bibliographic record

VenueJournal of Thoracic Disease · 2020
Typereview
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsMcGill University Health Centre
FundersRéseau de cancérologie Rossy
KeywordsMedicineLung cancerBronchoscopyPathologyBiopsyStage (stratigraphy)Molecular diagnosticsCompanion diagnosticRadiologyCancerOncologyBioinformaticsInternal medicine

Abstract

fetched live from OpenAlex

The treatment of advanced lung cancer has become increasingly personalized over the past decade as a result of the improved understanding of tumor molecular biology and anti-tumor immunity. An adequate tumor sample is central to targetable mutation analysis, and immunologic profiling. The majority of lung cancer patients currently present at an advanced disease stage, so that diagnosis and staging are largely based on small biopsy and cytology specimens. Flexible bronchoscopy techniques play a prominent role in the acquisition of these diagnostic specimens. This narrative review summarizes the available evidence with regards to the role of various conventional and advanced flexible bronchoscopy techniques in acquiring sufficient tissue for mutation analysis and programmed death-ligand 1 (PD-L1) testing.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.954
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.079
GPT teacher head0.463
Teacher spread0.384 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations14
Published2020
Admission routes2
Has abstractyes

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