Evidence map›Paper›PMID 40894121›Full record

ArticlemedRxiv : the preprint server for health sciences2025

A Better Way: Initial Acceptability Testing of Using Artificial Intelligence Tools to Accelerate Development of Trauma Clinical Guidance.

Gabriela Zavala Wong, Shannon Rosenauer, Chelsea Church, Diana Sherifali, Megan Racey, Katheryn Grider, Ashley N Moreno, Lacey N LaGrone, 2025 Design for Implementation (DFI) Authorship Group, Pamela Bixby and 50 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. 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

5 · Who and what money

Authors and funding

60 authors.

Gabriela Zavala WongORCID 0000-0003-3337-639X
Shannon Rosenauer
Chelsea Church
Diana Sherifali
Katheryn Grider
2025 Design for Implementation (DFI) Authorship Group
Pamela Bixby
Stephanie Bonne
Eileen M Bulger
James G Cain
Jennifer Chastek
Julia Roberts Coleman
Todd W Costantini
Nicholas Cozzi
Kimberly A Davis
Rochelle A Dicker
Warren C Dorlac
Erik Van Eaton
Evert Eriksson
Susan Evans
Shannon Marie Foster
Jeffrey M Goodloe
Elliott R Haut
Molly Jarman
Alyssa Johnson
Meera Kotagal
Morgan Krause
John C Kubasiak
Kelly Lang
Allison Barbara Leigh
Halinder S Mangat
Debra Marie Marvel
Christopher Paul Michetti
Vicki Moran
Ashley N Moreno
Simon Jw Oczkowski
Michael A Person
Michelle A Price
L J Punch
Megan Racey
Bradford L Ray
Diane Redmond
Linda Kate Reinhart
Heather Rhodes
Bryn Rhodes
Andres M Rubiano
Sabrina Sanchez
Babak Sarani
Erica Shelton
David A Spain
Kristan Staudenmayer
Deborah M Stein
Julie Valenzuela
Cynthia Lizette Villarreal
Jeffrey L Wells
Gabriela Zavala Wong
LeAnne Sitari Young

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Representatives of the trauma community have voiced a need for a new approach to developing clinical guidance. In this study, we test the initial acceptability of a proposed 12-step approach that aims to reduce the current clinical guidance timeline from more than 24 months to 24 weeks. Methods: Investigators hypothesized that artificial intelligence (AI) tools could be leveraged to improve and make the process of clinical guidance development more efficient, facilitating AI initial output that could later be reviewed by subject matter experts (SMEs). Ensuring ethical standards and a collaborative design. Following the agile methodology, emphasizing continuous delivery and improvement, and the Practical, Robust Implementation and Sustainability Model (PRISM) framework, the investigators drafted a 12-step approach to clinical guidance development in 24 weeks. The process starts with the selection of a clinical topic and culminates in a bedside-ready clinical decision tree. Results: The 2025 Design Conclusions: The current landscape of clinical guidance offers significant opportunities for improvement. Key areas for enhancement include promoting collaboration across multiple disciplines and organizations, developing recommendations that consider resource variations, and utilizing new technologies, such as AI, to expedite the development process. This is crucial because ongoing delays lead to practices lagging behind current evidence. Further research is needed to rigorously test and refine how responsible use of AI can be integrated into expediting evidence integration into clinical guidance. Key Messages:

Identifiers

PMID40894121
PMCPMC12393627

What OpenQuestion holds

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Registered trials

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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.