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Record W4224277138 · doi:10.3390/curroncol29040207

Randomized Controlled Trials in Lung, Gastrointestinal, and Breast Cancers: An Overview of Global Research Activity

2022· review· en· W4224277138 on OpenAlexaffvenue
J. Connor Wells, Adam Fundytus, Shubham Sharma, Wilma M. Hopman, Joseph C. Del Paggio, Bishal Gyawali, Deborah Mukherji, Nazik Hammad, C.S. Pramesh, Ajay Aggarwal, Richard Sullivan, Christopher M. Booth

Bibliographic record

VenueCurrent Oncology · 2022
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsNOSM UniversityQueen's University
FundersScience and Technology Facilities Council
KeywordsMedicineBreast cancerRandomized controlled trialLung cancerInternal medicineClinical endpointSurrogate endpointClinical trialOncologyCancer

Abstract

fetched live from OpenAlex

Background: In this study, we compared and contrasted design characteristics, results, and publications of randomized controlled trials (RCTs) in gastrointestinal (GI), lung, and breast cancer. Methods: A PUBMED search identified phase III RCTs of anticancer therapy in GI, lung, and breast cancer published globally during the period 2014−2017. Descriptive statistics, chi-square tests, and the Kruskal−Wallis test were used to compare RCT design, results, and output across the cancer sites. Results: A total of 352 RCTs were conducted on GI (36%), lung (29%), and breast (35%) cancer. Surrogate endpoints were used in 55% of trials; this was most common in breast trials (72%) compared to GI (47%) and lung trials (43%, p < 0.001). Breast trials more often met their primary endpoint (54%) than GI (41%) and lung trials (41%) (p = 0.024). When graded with the ESMO-MCBS, lung cancer trials (50%, 15/30) were more likely to meet the threshold for substantial benefit. GI trials were published in journals with a substantially lower impact factor (IF; median IF 13) than lung (median IF 21) and breast cancer trials (median IF 21) (p = 0.038). Conclusions: Important differences in RCT design and output exist between the three major cancer sites. Use of surrogate endpoints and the magnitude of benefit associated with new treatments vary substantially across cancer sites.

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)0.033
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.895
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0330.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0210.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.579
GPT teacher head0.538
Teacher spread0.040 · 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

Citations4
Published2022
Admission routes2
Has abstractyes

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