Evidence map›Paper›PMID 35025898›Full record

ArticlePloS one2022

Support Models for Addiction Related Treatment (SMART) for pregnant women: Study protocol of a cluster randomized trial of two treatment models for opioid use disorder in prenatal clinics.

Ariadna Forray, Amanda Mele, Nancy Byatt, Amalia Londono Tobon, Kathryn Gilstad-Hayden, Karen Hunkle, Suyeon Hong, Heather Lipkind, David A Fiellin, Katherine Callaghan and 1 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
3.4field-weighted citation impact, top 8% of its field
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

4 citing papers in PubMed, 9 citations in OpenAlex.

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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

11 authors at 3 institutions in 1 country.

Ariadna ForrayDepartment of Psychiatry, Yale School of Medicine, New Haven, Connecticut, United States of America.
Amanda MeleDepartment of Psychiatry, Yale School of Medicine, New Haven, Connecticut, United States of America.ORCID 0000-0002-9893-3225
Nancy ByattDepartment of Psychiatry, University of Massachusetts School of Medicine, Worcester, Massachusetts, United States of America.
Amalia Londono TobonDepartment of Psychiatry and Human Behavior, Brown University, Providence, Rhode Island, United States of America.
Kathryn Gilstad-HaydenDepartment of Psychiatry, Yale School of Medicine, New Haven, Connecticut, United States of America.
Karen HunkleDepartment of Psychiatry, Yale School of Medicine, New Haven, Connecticut, United States of America.
Suyeon HongDepartment of Psychiatry, Yale School of Medicine, New Haven, Connecticut, United States of America.ORCID 0000-0003-0200-7479
Heather LipkindDepartment of Obstetrics, Gynecology, and Reproductive Sciences, Yale School of Medicine, New Haven, Connecticut, United States of America.
David A FiellinDepartment of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America.
Katherine CallaghanDepartment of Ob/Gyn, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America.
Kimberly A YonkersDepartment of Psychiatry, University of Massachusetts School of Medicine, Worcester, Massachusetts, United States of America.
Yale University · USUniversity of Massachusetts Chan Medical School · USBrown University · US

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
NCATS NIH HHS UL1 TR001863
6 · The paper itself

Abstract

introductionThe prevalence of opioid use disorder (OUD) in pregnancy increased nearly five-fold over the past decade. Despite this, obstetric providers are less likely to treat pregnant women with medication for OUD than non-obstetric providers (75% vs 91%). A major reason is many obstetricians feel unprepared to prescribe medication for opioid use disorder (MOUD). Education and support may increase prescribing and overall comfort in delivering care for pregnant women with OUD, but optimal models of education and support are yet to be determined. METHODS AND ANALYSIS: We describe the rationale and conduct of a matched-pair cluster randomized clinical trial to compare the effectiveness of two models of support for reproductive health clinicians to provide care for pregnant and postpartum women with OUD. The primary outcomes of this trial are patient treatment engagement and retention in OUD treatment. This study compares two support models: 1) a collaborative care approach, based upon the Massachusetts Office-Based-Opioid Treatment Model, that provides practice-level training and support to providers and patients through the use of care managers, versus 2) a telesupport approach based on the Project Extension for Community Healthcare Outcomes, a remote education model that provides mentorship, guided practice, and participation in a learning community, via video conferencing. DISCUSSION: This clustered randomized clinical trial aims to test the effectiveness of two approaches to support practitioners who care for pregnant women with an OUD. The results of this trial will help determine the best model to improve the capacity of obstetrical providers to deliver treatment for OUD in prenatal clinics.

trial registrationClinicaltrials.gov trial registration number: NCT0424039.

Indexed as

Ambulatory Care FacilitiesAnalgesics, OpioidBehavior, AddictiveBuprenorphineFemaleHumansOpioid-Related DisordersPatient ParticipationPostpartum PeriodPregnancyPregnant PeopleAnalgesics, OpioidBuprenorphine

Identifiers

PMID35025898
PMCPMC8758001
OpenAlexW4206369699

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.