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Record W4380291366 · doi:10.2217/fon-2022-1289

Plain language summary of the updated results from the CROWN study comparing lorlatinib with crizotinib in people with advanced non-small-cell lung cancer

2023· article· en· W4380291366 on OpenAlexaff
Benjamin Solomon, Todd M. Bauer, Tony Mok, Geoffrey Liu, Julien Mazières, Filippo de Marinis, Yasushi Goto, Dong‐Wan Kim, Yi‐Long Wu, Jacek Jassem, Froylán López López, Ross A. Soo, Alice T. Shaw, Anna Polli, Rossella Messina, Laura Iadeluca, Francesca Toffalorio, Enriqueta Felip

Bibliographic record

VenueFuture Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsUniversity Health Network
FundersNational Comprehensive Cancer NetworkPfizer
KeywordsCrizotinibMedicineLung cancerCrown (dentistry)OncologyInternal medicineDentistry

Abstract

fetched live from OpenAlex

What is this summary about? This summary shows the updated results of an ongoing research study called CROWN that was published in The Lancet Respiratory Medicine in December 2022. In the CROWN study, researchers looked at the effects of two study medicines called lorlatinib and crizotinib. The study included people with advanced non-small-cell lung cancer (NSCLC) that had not been treated previously. All people in the study had cancer cells with changes (known as alterations) in a gene called anaplastic lymphoma kinase, or ALK. This ALK gene is involved in cancer growth. In this updated study, researchers looked at the continued benefit in people who took lorlatinib compared with people who took crizotinib after 3 years.What did this study find? After 3 years of being observed, people who took lorlatinib were more likely to be alive without their cancer getting worse than people who took crizotinib. At 3 years, 64% of people who took lorlatinib were alive without their cancer getting worse compared with 19% of people who took crizotinib. The cancer was less likely to have spread within or to the brain in people who took lorlatinib than in people who took crizotinib. After 3 years of being observed, 61% of people were still taking lorlatinib and 8% of people were still taking crizotinib. People who took lorlatinib had more severe side effects than people who took crizotinib. However, these side effects were manageable. The most common side effects with lorlatinib were high levels of cholesterol or high levels of triglycerides (a type of fat) in the blood. Life-threatening side effects were seen in 13% of people who took lorlatinib and 8% in crizotinib. Two people who took lorlatinib died because of side effects from lorlatinib.What do the results of the study mean? The updated results from the CROWN study showed that a larger percentage of people who took lorlatinib continued to benefit from their treatment after being observed for 3 years compared with those who took crizotinib.This is an abstract of the Plain Language Summary of Publication article. To read the full Plain Language Summary of this article, click here to view the PDF.Link to original article here

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)0.010
metaresearch head score (Gemma)0.071
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.074
Threshold uncertainty score0.248

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.071
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.008
Bibliometrics0.0060.010
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0040.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0740.020

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 head0.011
GPT teacher head0.325
Teacher spread0.314 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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