Evidence map›Paper›PMID 32031766›Full record

ArticleCanadian journal of surgery. Journal canadien de chirurgie2020

A novel online education module to teach clinicians how to correctly identify ultrasonographic features of mediastinal lymph nodes during endobronchial ultrasound

Danielle A. Hylton, Yaron Shargall, Christian Finley, John Agzarian, Christine Fahim, Waël C. Hanna

Open access · diamondAbstract read
In one paragraph

Article in Canadian journal of surgery. Journal canadien de chirurgie, 2020. 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
1.4field-weighted citation impact, top 16% 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, 12 citations in OpenAlex.

  1. Article
  2. 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

6 authors at 2 institutions in 1 country.

Danielle A. HyltonFrom the Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ont. (Hylton, Fahim, Hanna); and the Division of Thoracic Surgery, Department of Surgery, McMaster University, Hamilton, Ont. (Shargall, Agzarian, Hanna).
Yaron ShargallFrom the Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ont. (Hylton, Fahim, Hanna); and the Division of Thoracic Surgery, Department of Surgery, McMaster University, Hamilton, Ont. (Shargall, Agzarian, Hanna).
Christian FinleyFrom the Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ont. (Hylton, Fahim, Hanna); and the Division of Thoracic Surgery, Department of Surgery, McMaster University, Hamilton, Ont. (Shargall, Agzarian, Hanna).
John AgzarianFrom the Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ont. (Hylton, Fahim, Hanna); and the Division of Thoracic Surgery, Department of Surgery, McMaster University, Hamilton, Ont. (Shargall, Agzarian, Hanna).
Christine FahimFrom the Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ont. (Hylton, Fahim, Hanna); and the Division of Thoracic Surgery, Department of Surgery, McMaster University, Hamilton, Ont. (Shargall, Agzarian, Hanna).
Waël C. HannaFrom the Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ont. (Hylton, Fahim, Hanna); and the Division of Thoracic Surgery, Department of Surgery, McMaster University, Hamilton, Ont. (Shargall, Agzarian, Hanna).
Impact · CAMcMaster University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Ultrasonographic features can be used to predict mediastinal lymph node malignancy during endobronchial ultrasonography. Despite the validity of using these features for this purpose, the features are not being widely used in clinical practice. This may be attributable to the absence of educational programs that teach clinicians how to identify the features. To address this knowledge gap, we developed an online educational module to teach clinicians how to correctly interpret ultrasonographic features. Methods: The module was designed using corrective feedback and test-enhanced learning theories and distributed to clinicians in relevant specialties. The efficacy of the program was determined by comparing the percentages of correctly identified ultrasonographic features as each clinician progressed through the module. Participants were also asked to self-rate their confidence during the module. Analysis of variance was conducted, and a learning curve and descriptive statistics were generated. Results: Twenty-two of the 29 participants (76%) completed the module. Analysis of variance indicated that the percentage of correctly identified features increased significantly as clinicians completed the module (p = 0.004); this finding is supported by the positive slope of the learning curve. Even though they initially reported some difficulty with identifying certain features, their confidence increased as they progressed through the module. When asked, 86% of participants reported that they found the educational module helpful and 90% reported that they would recommend it to others. Conclusion: Participating clinicians were receptive to the interactive educational module. It enhances clinician skill and confidence in interpreting ultrasonographic features. The results of this study provide the foundation needed to test the validity of the educational module in clinical settings and to further explore clinician preferences for educational programs.

Indexed as

BronchoscopyClinical CompetenceEducation, DistanceEducation, MedicalEndosonographyPulmonologistsSurgeonsAdultHumansInternship and ResidencyLymph NodesMediastinumMedical Staff, HospitalThoracic Neoplasms

Identifiers

PMID32031766
PMCPMC7828937
OpenAlexW3009761809

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.