Evidence map›Paper›PMID 38076212›Full record

ArticleHealth equity2023

Critical Appraisal for Racial and Ethnic Equity in Clinical Prediction Models Extension: Development of a Critical Appraisal Tool Extension to Assess Racial and Ethnic Equity-Related Risk of Bias for Clinical Prediction Models.

Shazia M Siddique, Corinne V Evans, Michael Harhay, Eric S Johnson, Jaya Aysola, Gary E Weissman, Nikhil K Mull, Emilia Flores, Harald Schmidt, Kelley Tipton and 2 more

Abstract read
In one paragraph

Article in Health equity, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 2 pooled it
–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

3 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Guideline
  3. Article
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

12 authors.

Shazia M SiddiqueDivision of Gastroenterology, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.ORCID https://orcid.org/0000-0002-8608-3374
Corinne V EvansKaiser Permanente Evidence-Based Practice Center, Kaiser Permanente Center for Health Research, Portland, Oregon, USA.ORCID https://orcid.org/0009-0003-0530-0539
Michael HarhayDepartment of Biostatics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Eric S JohnsonKaiser Permanente Evidence-Based Practice Center, Kaiser Permanente Center for Health Research, Portland, Oregon, USA.
Jaya AysolaPenn Medicine Center for Health Equity Advancement, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA.
Gary E WeissmanPulmonary, Allergy, and Critical Care Division, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Nikhil K MullCenter for Evidence-Based Practice, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA.
Emilia FloresCenter for Evidence-Based Practice, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA.
Harald SchmidtDepartment of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Kelley TiptonECRI, Plymouth Meeting, Pennsylvania, USA.
Brian LeasCenter for Evidence-Based Practice, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA.
Jennifer S LinKaiser Permanente Evidence-Based Practice Center, Kaiser Permanente Center for Health Research, Portland, Oregon, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Despite mounting evidence that the inclusion of race and ethnicity in clinical prediction models may contribute to health disparities, existing critical appraisal tools do not directly address such equity considerations. Objective: This study developed a critical appraisal tool extension to assess algorithmic bias in clinical prediction models. Methods: A modified e-Delphi approach was utilized to develop and obtain expert consensus on a set of racial and ethnic equity-based signaling questions for appraisal of risk of bias in clinical prediction models. Through a series of virtual meetings, initial pilot application, and an online survey, individuals with expertise in clinical prediction model development, systematic review methodology, and health equity developed and refined this tool. Results: Consensus was reached for ten equity-based signaling questions, which led to the development of the Critical Appraisal for Racial and Ethnic Equity in Clinical Prediction Models (CARE-CPM) extension. This extension is intended for use along with existing critical appraisal tools for clinical prediction models. Conclusion: CARE-CPM provides a valuable risk-of-bias assessment tool extension for clinical prediction models to identify potential algorithmic bias and health equity concerns. Further research is needed to test usability, interrater reliability, and application to decision-makers.

Indexed as

critical appraisalguideline developmenthealth equityrisk of biassystematic review

Identifiers

PMID38076212
PMCPMC10698767

What OpenQuestion holds

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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.