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Record W4200254289 · doi:10.1016/j.jtocrr.2021.100269

Impact of the Coronavirus Disease 2019 Pandemic on Global Lung Cancer Clinical Trials: Why It Matters to People With Lung Cancer

2021· article· en· W4200254289 on OpenAlexaboutno aff
Upal Roy, Anne‐Marie Baird, A. Ciupek, J. Fox, Eugene Manley, Kim Norris, Giorgio V. Scagliotti, Heather A. Wakelee, Tetsuya Mitsudomi, Russell Clark, Renee Arndt, Fred R. Hirsch, Paul A. Bunn, Matthew Smeltzer

Bibliographic record

VenueJTO Clinical and Research Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsnot available
FundersFoundation MedicineGenentechMerck Sharp and DohmeEli Lilly and CompanyOno PharmaceuticalDaiichi-SankyoBeiGeneTG TherapeuticsRegeneron PharmaceuticalsJohnson and JohnsonTakeda Pharmaceutical CompanyAmgenBoehringer IngelheimChugai PharmaceuticalRocheNovartisSanofiMerckPfizerG1 TherapeuticsDaiichi Sankyo EuropeBayerAstraZenecaBristol-Myers Squibb
KeywordsLung cancerMedicineClinical trialCancerDiseasePandemicInternal medicineOncologyIntensive care medicineFamily medicineCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The past decade of lung cancer research has seen rapid advances in early detection and treatment and many new Food and Drug Administration–approved therapies for lung cancer. This has largely been possible because of clinical trials. Therapeutic, interventional clinical trials have become a critical component of lung cancer care. The National Comprehensive Cancer Network, the American Society of Clinical Oncology, and the European Society for Medical Oncology guidelines support clinical trial enrollment as standard of care for people with advanced-stage NSCLC and extensive-stage SCLC in first- and subsequent-line settings.

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)0.315
metaresearch head score (Gemma)0.647
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.685
Threshold uncertainty score0.845

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3150.647
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0060.008
Bibliometrics0.0040.009
Science and technology studies0.0020.005
Scholarly communication0.0120.016
Open science0.0040.004
Research integrity0.0100.009
Insufficient payload (model declined to judge)0.0140.002

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 head0.246
GPT teacher head0.608
Teacher spread0.363 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainMethods
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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

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