Evidence map›Paper›PMID 41952780›Full record

ArticleContemporary clinical trials communications2026

Improving representativeness in trial recruitment: A data-driven approach.

Pooyan Khajehpour, Jonathan Amar, Krisda H Chaiyachati, Yugang Jsaiz, Sina Rashidian, Erich Huang, Vindell Washington

Abstract read
In one paragraph

Article in Contemporary clinical trials communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Pooyan KhajehpourVerily Life Sciences, South San Francisco, CA, USA.
Jonathan AmarVerily Life Sciences, South San Francisco, CA, USA.
Krisda H ChaiyachatiVerily Life Sciences, South San Francisco, CA, USA.
Yugang JsaizVerily Life Sciences, South San Francisco, CA, USA.
Sina RashidianVerily Life Sciences, South San Francisco, CA, USA.
Erich HuangVerily Life Sciences, South San Francisco, CA, USA.
Vindell WashingtonVerily Life Sciences, South San Francisco, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Persistent under-representation of certain racial, ethnic, or socio-demographic subgroups in clinical studies continues to put patients at risk for unfavorable health outcomes. In an environment where accrual increasingly relies on digital campaigns, we propose applying data-driven approaches to the management of campaign budgets. Intentional budget management approaches can integrate representativeness goals into research participant recruitment, thereby facilitating enrollment of diverse populations into clinical studies. Our concept consists of applying a reinforcement learning approach to allocate resources towards digital marketing channels dynamically, using a restless multi-armed bandit model. With this approach it is possible to generate sequential resource allocation strategies based on information about the size and parameter distribution of the target cohorts. Those strategies pursue the maximization of accrual numbers

Indexed as

Clinical trial recruitmentHealth equityResearch equity

Identifiers

PMID41952780
PMCPMC13054061

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.