Evidence map›Paper›PMID 29317849›Full record

ArticleRisk management and healthcare policy2018

Is medical treatment of Alcohol Withdrawal Syndrome a Stag Hunt? Challenges and opportunities in managing risk and uncertainty in addiction cessation.

Roger Lee Mendoza

Abstract read
In one paragraph

Article in Risk management and healthcare policy, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

1 author.

Roger Lee MendozaSchool of Business, Wilmington University, New Castle, DE, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeWhile the individual and social costs of alcoholism or alcohol use disorder are well established, few are aware that medical problems can arise during detoxification, some of which can be life-threatening. This study determines if sustained treatment for Alcohol Withdrawal Syndrome (AWS) might be based on the strategic choices and expectations of patients and health care providers alike, as well as the risk mitigation options available to them. DESIGN/APPROACH: AWS was modeled as a Stag Hunt to explain both risk and decision-making in medical treatments for detoxification, since it can deduce a set of equilibrium strategies available to both patient and provider. Modeling was based on a review of juried literature gathered from search engines with the use medical subject heading terms. MAIN

findingsWhile there is little evidence that decision-making is shared between patient and physician in AWS treatments, the outcomes of their interactions depend on utility-maximizing choices each makes in anticipation of the other. Payoff-dominant and risk-dominant treatment outcomes are equally likely and equally cost-efficient, as conditioned by the presence (or absence) of mutual trust and assurance in reciprocal transactions. CONCLUSION/VALUE: Simulation games, such as the Stag Hunt, offer a viable framework to understand patient and provider incentives and health-affecting behaviors during treatments for addiction cessation. If both anticipate indefinitely interacting in the absence of any predetermined or foreseeable final visit, they can maximize future payoffs from mutual cooperation and accountability, which fosters health promotion. However, this study suggests that the effect of cooperation is distinct from the effect of time in AWS and other addiction-cessation programs.

Indexed as

health-affecting behaviorshealthcare providerpatient compliancepayoffrelapsesimulation gametreatment disruption/discontinuation

Identifiers

PMID29317849
PMCPMC5743126

What OpenQuestion holds

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LicenceCC BY-NC
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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.