Evidence map›Paper›PMID 36449293›Full record

ArticleJAMA network open2022

Accuracy in Patient Understanding of Common Medical Phrases.

Rachael Gotlieb, Corinne Praska, Marissa A Hendrickson, Jordan Marmet, Victoria Charpentier, Emily Hause, Katherine A Allen, Scott Lunos, Michael B Pitt

Open access · goldAbstract read
In one paragraph

Article in JAMA network open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 4 pooled it
15.7field-weighted citation impact, top 1% 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

49 citing papers in PubMed, 4 syntheses or guidelines pooled it, 101 citations in OpenAlex.

  1. Pooled it
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  3. MetaAnatomical sciences education · 2025
    Pooled it
  4. Communicating blood test results in primary care: a mixed-methods systematic review.The British journal of general practice : the journal of the Royal College of General Practitioners · 2025
    Pooled it
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  7. Communication barriers for d/Deaf people with cancer: a scoping review.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
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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

9 authors at 3 institutions in 1 country.

Rachael GotliebUniversity of Minnesota Medical School, Minneapolis.
Corinne PraskaUniversity of Minnesota Medical School, Minneapolis.
Marissa A HendricksonDepartment of Pediatrics, University of Minnesota, Minneapolis.
Jordan MarmetDepartment of Pediatrics, University of Minnesota, Minneapolis.
Victoria CharpentierUniversity of Minnesota Medical School, Minneapolis.
Emily HauseDepartment of Pediatrics, University of Minnesota, Minneapolis.
Katherine A AllenDepartment of Pediatrics, University of Minnesota, Minneapolis.
Scott LunosBiostatistical Design and Analysis Center, Clinical and Translational Science Institute, University of Minnesota, Minneapolis.
Michael B PittDepartment of Pediatrics, University of Minnesota, Minneapolis.
University of Minnesota · USUniversity of Minnesota Medical Center · USFairview Health Services · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Despite acknowledging that medical jargon should be avoided, health care practitioners frequently use it when communicating with patients. Objective: To characterize the understanding of common medical jargon terms by surveying a cross section of the general public and studying phrases that have established meanings in regular usage but different meanings in a medical context (eg, negative and positive test results). Design, Setting, and Participants: In this cross-sectional study, participants indicated their understanding of phrases that may have different meanings in medicine than in colloquial English via a mix of short answer and multiple choice questions. Several questions included paired phrases to assess for differences in understanding with or without jargon. Volunteers were recruited at the 2021 Minnesota State Fair near St Paul, Minnesota. An electronic survey was given to a volunteer sample of 215 adults (>18 years) who did not work or train to work in the medical field and spoke and read English. Exposures: Completing a written or verbal survey. Main Outcomes and Measures: The main outcome was an accurate understanding of the medical terminology. Free-text responses were coded by 2 researchers for comprehension. Secondary outcomes looked for associations between volunteer demographics and understanding. Results: The 215 respondents (135 [63%] female; mean [SD] age, 42 [17] years) demonstrated a varied ability to interpret medical jargon phrases. For example, most participants (207 [96%]) knew that negative cancer screening results meant they did not have cancer, but fewer participants (143 [79%]) knew that the phrase "your tumor is progressing" was bad news, or that positive lymph nodes meant the cancer had spread (170 [67%]). While most (171 [80%]) recognized that an unremarkable chest radiography was good news, only 44 participants (21%) correctly understood that a clinician saying their radiography was impressive was generally bad news. In each of the paired phrases comparing jargon vs nonjargon approaches, the nonjargon phrase was understood significantly better (P < .001). Conclusions and Relevance: These findings suggest that several common phrases are misunderstood when used in a medical setting, with the interpreted meaning frequently the exact opposite of what is intended.

Indexed as

MedicineNegative ResultsAdultCross-Sectional StudiesFemaleHumansMaleResearch DesignVolunteers

Identifiers

PMID36449293
PMCPMC9713608
OpenAlexW4310954861

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.