Evidence map›Paper›PMID 26186668›Full record

ArticleJournal of palliative medicine2015

The Language of End-of-Life Decision Making: A Simulation Study.

Annie Lu, Deepika Mohan, Stewart C Alexander, Craig Mescher, Amber E Barnato

Open access · greenAbstract readMulticenter Study
In one paragraph

Article in Journal of palliative medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
2.6field-weighted citation impact, top 11% 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

15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 35 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

5 authors at 5 institutions in 1 country.

Annie Lu1 Department of Pediatrics and Communicable Diseases, University of Michigan , Ann Arbor, Michigan.
Deepika Mohan2 Department of Critical Care Medicine, University of Pittsburgh School of Medicine , Pittsburgh, Pennsylvania.
Stewart C Alexander3 Department of Consumer Science, Purdue University , West Lafayette, Indiana.
Craig Mescher4 Department of Medicine, Division of Hematology, Oncology, and Transplantation, University of Minnesota , Minneapolis, Minnesota.
Amber E Barnato5 Section of Decision Sciences, Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine , Pittsburgh, Pennsylvania.
Decision Sciences (United States) · USPurdue University West Lafayette · USUniversity of Michigan–Ann Arbor · USUniversity of Minnesota · USUniversity of Pittsburgh · US

Funding

University of Pittsburgh Clinical and Translational Science InstituteUL1TR000005 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI REIS, STEVEN E · 2012 to 2015
$50.8M
University of Pittsburgh Clinical and Translational Science InstitutionKL2TR000146 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI REIS, STEVEN E · 2012 to 2015
$8.7M
Time-pressured decision making in trauma triageK23GM101292 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI MOHAN, DEEPIKA · 2012 to 2014
$561k
Isolating Mechanisms Underlying Hospital Variation in End-of-Life ICU UseR21CA141093 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BARNATO, AMBER E · 2009 to 2010
$385k
ICU Triage Decisions for Elders with End Stage Cancer: the Role of Patient RaceR21CA139264 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BARNATO, AMBER E · 2009 to 2010
$381k
NCATS NIH HHS KL2 TR000146NCATS NIH HHS UL1 TR000005NCI NIH HHS R21CA139264NCI NIH HHS R21CA141093NIGMS NIH HHS K23 GM101292
6 · The paper itself

Abstract

backgroundFraming is known to influence decision making.

objectiveThe study objective was to describe language used by physicians when discussing treatment options with a critically and terminally ill elder.

methodsHigh-fidelity simulation was used, involving an elder with end-stage cancer and life-threatening hypoxia, followed by a debriefing interview. Subjects were hospitalist, emergency medicine, and critical care physicians from three academic medical centers. Measures were observation of encounters in real time followed by content analysis of simulation and debriefing interview transcripts. During the simulation we identified the first mention ("broaching") of principal treatment options--intubation and mechanical ventilation (life-sustaining treatment [LST]) and palliation in anticipation of death (palliation)--and used constant comparative methods to identify language used. We identified physician opinions about the use of LST in this clinical context during the debriefing interviews, and compared language used with opinions.

resultsAmong 114 physician subjects, 106 discussed LST, 86 discussed palliation, and 84 discussed both. We identified five frames: will (decided), must (necessary), should (convention), could (option), and ask (elicitation of preferences). Physicians broached LST differently than palliation (p<0.01), most commonly framing LST as necessary (53%), while framing palliation as optional (49%). Among physicians who framed LST as imperative (will or must), 16 (30%) felt intubation would be inappropriate in this clinical situation.

conclusionsIn this high-fidelity simulation experiment involving a critically and terminally ill elder, the majority of physicians framed the available options in ways implying LST was the expected or preferred choice. Framing of treatment options could influence ultimate treatment decisions.

Indexed as

CommunicationDecision MakingPalliative CarePatient SimulationPhysician-Patient RelationsTerminal CareAcademic Medical CentersAgedAttitude of Health PersonnelCritical CareEmergency MedicineHospitalistsHumansInterviews as TopicPractice Patterns, Physicians'

Identifiers

PMID26186668
PMCPMC4696426
OpenAlexW1798832679

What OpenQuestion holds

Textmetadata
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.