Evidence map›Paper›PMID 42032114›Full record

ArticleNPJ digital medicine2026

Exploring the limits of localization: federated model stacking improves hospital-level prediction in a national research network.

Jie Cao, Emily Balczewski, Rahul Ladhania, Paramveer Dhillon, Donald S Likosky, Allison M Janda, MPOG Collaborators, Sachin Kheterpal, Michael R Mathis, Karandeep Singh

Abstract read
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In one paragraph

Article in NPJ digital medicine, 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

10 authors.

Jie CaoJacobs Center for Health Innovation, UC San Diego Health, San Diego, CA, USA.
Emily BalczewskiDepartment of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
Rahul LadhaniaDepartment of Health Management and Policy, University of Michigan, Ann Arbor, MI, USA.
Paramveer DhillonSchool of Information, University of Michigan, Ann Arbor, MI, USA.
Donald S LikoskyDepartment of Cardiac Surgery, University of Michigan Medical School, Ann Arbor, MI, USA.
Allison M JandaDepartment of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA.
MPOG Collaborators
Sachin KheterpalDepartment of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA.
Michael R Mathis *Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
Karandeep Singh *Jacobs Center for Health Innovation, UC San Diego Health, San Diego, CA, USA. karandeep@ucsd.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Challenges with model generalizability and data privacy have led to a shift in health artificial intelligence (AI) models being trained locally within individual health systems rather than relying on multicenter data. Localization carries the promise of capturing local practice patterns and patient demographics, presumably resulting in better models. Our study empirically tests this hypothesis in a national research network by comparing locally trained models predicting acute kidney injury (AKI) after cardiac surgery with two multicenter modeling approaches, pooling and a novel federated model stacking method. Trained on 43,926 cases across 23 hospitals, the study finds that multicenter models outperform single-center approaches, with higher area under the receiver operating characteristic curves (AUCs) for all AKI severity levels in both temporal and external validation sets. Hospitals with smaller case volumes benefit the most from multicenter approaches, showing the greatest AUC increase over locally trained models.

Identifiers

PMID42032114
PMCPMC13314945

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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.