Evidence map›Paper›PMID 42543496›Full record

ArticleClinical and translational science2026

Temporal Drivers of Opioid-Related ED Visits: An Ensemble Machine Learning Study With Consensus-Based Feature Attribution.

R Jerome Dixon, Elvin T Price

Abstract read
In one paragraph

Article in Clinical and translational science, 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

2 authors.

R Jerome DixonDepartment of Pharmacotherapy and Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia, USA.ORCID 0000-0001-8622-0597
Elvin T PriceDepartment of Pharmacotherapy and Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia, USA.ORCID 0000-0001-6046-7540

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identification of modifiable risk factors for prescription opioid use disorder (OUD)-related emergency department (ED) visits (ICD-10 F11.xx) is a clinical priority; however, most published models remain cross-sectional and lack pharmacogenomic (PGx) enrichment or dual-method feature confirmation. Using Virginia All-Payer Claims Database (APCD) data (2016-2019; 6,929,576 patients), we analyzed prescription OUD-related ED visits (

Indexed as

Analgesics, OpioidEmergency Room VisitsEmergency Service, HospitalMachine LearningOpioid-Related DisordersAdolescentAdultBoosting Machine Learning AlgorithmsFemaleHumansMaleMiddle AgedPredictive Learning ModelsRisk FactorsTime FactorsVirginiaAnalgesics, Opioidemergency departmentmachine learningobservational risk attributionopioid use disorderpharmacogenomicsSHAPtrajectory analysis

Identifiers

PMID42543496
PMCPMC13429803

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.