Borrowing From the Future: Enhancing Early Risk Assessment through Contrastive Learning.

Minghui Sun, Matthew M Engelhard, Benjamin A Goldstein

Abstract read

In one paragraph

Article in Proceedings of machine learning research, 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

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

3 authors.

Minghui SunDepartment of Biostatistics & Bioinformatics, Duke University, Durham, NC, USA.

Matthew M EngelhardDepartment of Biostatistics & Bioinformatics, Duke University, Durham, NC, USA.

Benjamin A GoldsteinDepartment of Biostatistics & Bioinformatics, Duke University, Durham, NC, USA.

Funding

Support for QA/QC for Prior Approval ProcessUL1TR002553 · NCATS · DUKE UNIVERSITY · PI LI, JENNIFER S, MCNAMARA, JAMES O. · 2018 to 2023

$58.5M

Recruitment and Assessment CoreP50HD093074 · NICHD · DUKE UNIVERSITY · PI Kimberly L H Carpenter · 2017 to 2026

$27.6M

Machine Learning Methods to Develop and Deploy Real-Time Risk Surveillance for Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder from the Electronic Health RecordK01MH127309 · NIMH · DUKE UNIVERSITY · PI Matthew Morrow Engelhard · 2022 to 2026

$842k

Clinical and host microbiome features in the development of acute otitis mediaK01AI173398 · NIAID · DUKE UNIVERSITY · PI Jillian Heyward Hurst · 2023 to 2026

$509k

NCATS NIH HHS UL1 TR002553NIAID NIH HHS K01 AI173398NICHD NIH HHS P50 HD093074NIMH NIH HHS K01 MH127309

6 · The paper itself

Abstract

Risk assessments for a pediatric population are often conducted across multiple stages. For example, clinicians may evaluate risks prenatally, at birth, and during WellChild visits. While predictions at later stages typically achieve higher accuracy, it is clinically desirable to make reliable risk assessments as early as possible. Therefore, this study focuses on enhancing prediction performance in early-stage risk assessments. Our solution,

Identifiers

PMID41306332

PMCPMC12646567

What OpenQuestion holds

Textmetadata

LicenceTDM

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.