Evidence map›Paper›PMID 37592369›Full record

ArticleAddiction science & clinical practice2023

Validating opioid use disorder diagnoses in administrative data: a commentary on existing evidence and future directions.

Jeffrey F Scherrer, Mark D Sullivan, Marc R LaRochelle, Richard Grucza

Abstract read
In one paragraph

Article in Addiction science & clinical practice, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 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

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

4 authors.

Jeffrey F ScherrerDepartment of Family and Community Medicine, Saint Louis University School of Medicine, 1402 South Grand Blvd, St. Louis, MO, USA. jeffrey.scherrer@health.slu.edu.ORCID http://orcid.org/0000-0002-9148-2863
Mark D SullivanDepartment of Psychiatry and Behavioral Science, University of Washington School of Medicine, 1959 NE Pacific Street, Seattle, WA, 98195, USA.
Marc R LaRochelleClinical Addiction Research and Education Unit, Section of General Internal Medicine, School of Medicine and Boston Medical Center, Boston University, Boston, MA, USA.
Richard GruczaDepartment of Family and Community Medicine, Saint Louis University School of Medicine, 1402 South Grand Blvd, St. Louis, MO, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA valid opioid use disorder (OUD) identification algorithm for use in administrative medical record data would enhance investigators' ability to study consequences of OUD, OUD treatment seeking and treatment outcomes. MAIN BODY: Existing studies indicate ICD-9 and ICD-10 codes for opioid abuse and dependence do not accurately measure OUD. However, critical appraisal of existing literature suggests alternative validation methods would improve the validity of OUD identification algorithms in administrative data. Chart abstraction may not be sufficient to validate OUD, and primary data collection via structured diagnostic interviews might be an ideal gold standard. CONCLUSION AND COMMENTARY: Generating valid OUD identification algorithms is critical for OUD research and quality measurement in real world health care settings.

Indexed as

AlgorithmsOpioid-Related DisordersData CollectionHumansResearch DesignAdministrative dataOpioidsOpioid use disorderValidity

Identifiers

PMID37592369
PMCPMC10433556

What OpenQuestion holds

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Registered trials

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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.