MétaCan
Menu
Back to cohort
Record W4379599383 · doi:10.2217/fon-2022-0974

Comparison of tumor-agnostic and tumor-specific clinical oncology trial designs: a systematic review and meta-analysis

2023· review· en· W4379599383 on OpenAlexaff
Mufiza Farid‐Kapadia, Madelyn Barton, Zoë Bider-Canfield, Parneet Cheema, Bishal Gyawali, Natalie Nightingale, Lidija Latifovic, Henry Jacob Conter

Bibliographic record

VenueFuture Oncology · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsQueen's UniversityWilliam Osler Health SystemRoche (Canada)
FundersF. Hoffmann-La Roche
KeywordsMedicineClinical trialCancerOncologyInternal medicineMeta-analysisLung cancer

Abstract

fetched live from OpenAlex

Aim: To examine whether tumor-specific and tumor-agnostic oncology trials produce comparable estimates of objective response rate (ORR) in BRAF-altered cancers. Materials & methods: Electronic database searches were performed to identify phase I–III clinical trials testing tyrosine kinase inhibitors from 2000 to 2021. A random-effects model was used to pool ORRs. A total of 22 cohorts from five tumor-agnostic trials and 41 cohorts from 27 tumor-specific trials had published ORRs. Results: There was no significant difference between pooled ORRs from either trial design for multitumor analyses (37% vs 50%; p = 0.05); thyroid cancer (57% vs 33%; p = 0.10); non-small-cell lung cancer (39% vs 53%; p = 0.18); or melanoma (55% vs 51%; p = 0.58). Conclusion: For BRAF-altered advanced cancers, tumor-agnostic trials do not yield substantially different results from tumor-specific trials.

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)0.039
metaresearch head score (Gemma)0.072
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.039
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.072
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0160.029
Bibliometrics0.0050.005
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.270
GPT teacher head0.484
Teacher spread0.214 · 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 designMeta-analysis
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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueFuture OncologySame topicCancer Genomics and DiagnosticsFrench-language works237,207