Evidence map›Paper›PMID 35220332›Full record

ArticleJournal of addiction medicine

Take-home Naloxone at Opioid Treatment Programs: A Lifesaver.

Joanna G Katzman, Snehal Bhatt, George D Comerci

Open access · hybridAbstract read
In one paragraph

Article in Journal of addiction medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 33% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Review
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 at 1 institution in 1 country.

Joanna G KatzmanFrom the Department of Neurosurgery, University of New Mexico Health Sciences Center, (UNMHSC), Albuquerque, NM (JGK); UNMHSC, ECHO Institute (JGK, SB, GDC); Department of Psychiatry, UNMHSC, Albuquerque, NM (SB); and Department of Internal Medicine, UNMHSC, Albuquerque, NM (GDC).
Snehal Bhatt
George D Comerci
Greenside Design Center College of Design · ZA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Opioid-related overdose deaths have increased almost 30% in the US since the COVID-19 pandemic began. Tragically, many of these deaths could be prevented with widespread availability of naloxone. One innocuous harm-reduction strategy would be the federal government mandating the provision of take-home naloxone and brief overdose education to patients at opioid treatment programs. Take-home naloxone, for instance, may be used by a friend or a family member to save the life of the patient receiving treatment for opioid use disorder. Importantly, many studies demonstrate that patients receiving take-home naloxone at an opioid treatment program will use the naloxone to reverse an overdose of someone in their social network. Other successful indications for mandated take-home naloxone include: federal inmates leaving incarceration if they have an opioid substance use disorder diagnosis and federal police officers on active duty. This editorial describes the various organizations, medical societies, and governmental agencies who may consider making robust actionable recommendations regarding naloxone for persons with opioid use disorder. The authors strongly recommend that professional organizations include take-home naloxone as a best practice for any patient who may be at an elevated risk for an opioid overdose.

Indexed as

COVID-19Drug OverdoseOpiate OverdoseOpioid-Related DisordersAnalgesics, OpioidHumansNaloxoneNarcotic AntagonistsPandemicsAnalgesics, OpioidNaloxoneNarcotic Antagonists

Identifiers

PMID35220332
PMCPMC9653102
OpenAlexW4214545378

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.