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Racial and Ethnic Composition of Cancer Clinical Drug Trials: How Diverse Are We?

2017· article· en· W2781449980 on OpenAlexaboutno aff
Leslie J. Dickmann, Jennifer L. Schutzman

Bibliographic record

VenueThe Oncologist · 2017
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsnot available
FundersMicrosoft
KeywordsMedicineClinical trialEthnic groupDrugCancerClinical researchPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

Many approved drugs demonstrate different pharmacokinetics, pharmacodynamics, and/or safety across racial and ethnic groups. The primary objective of the current study was to summarize the racial and ethnic makeup of cancer clinical drug trials using cancer drugs approved by the U.S. Food and Drug Administration (FDA) between January 1, 2010, and July 31, 2016. In clinical studies used for FDA approvals, 82.3% of participants identified as white, 10.2% as Asian, 2.3% as black, and 4.7% as Hispanic. Black participants made up 7.7% of U.S. and Canadian cancer clinical drug trials and 2.6% of global cancer clinical drug trials while Asian participants made up 13.5% of global cancer clinical drug trials but only 1.8% of U.S. and Canadian cancer clinical drug trials. The current study indicates that although cancer clinical drug trials have become more inclusive of Asian participants, other racial and ethnic minority groups remain under-represented. This may result in an inadequate understanding of drug safety and efficacy in many racial and ethnic populations.

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)0.018
metaresearch head score (Gemma)0.043
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.874
GPT teacher head0.719
Teacher spread0.155 · 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 designObservational
Domainnot available
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

Citations23
Published2017
Admission routes1
Has abstractyes

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