Evidence map›Paper›PMID 38586126›Full record

ArticleFrontiers in digital health2023

Improving shared decision making for lung cancer treatment by developing and validating an open-source web based patient decision aid for stage I-II non-small cell lung cancer.

Iva Halilaj, Anshu Ankolekar, Anouk Lenaers, Avishek Chatterjee, Cary J G Oberije, Lisanne Eppings, Hans J M Smit, Lizza E L Hendriks, Arthur Jochems, Relinde I Y Lieverse and 3 more

Abstract read
In one paragraph

Article in Frontiers in digital health, 2023. 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
–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

2 citing papers in PubMed.

  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

13 authors.

Iva HalilajThe D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Netherlands.
Anshu AnkolekarThe D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Netherlands.
Anouk LenaersThe D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Netherlands.
Avishek ChatterjeeThe D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Netherlands.
Cary J G OberijeKheiron Medical Technologies, London, United Kingdom.
Lisanne EppingsThe D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Netherlands.
Hans J M SmitRijnstate Hospital, Arnhem, Netherlands.
Lizza E L HendriksDepartment of Pulmonary Diseases, GROW School for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, Netherlands.
Arthur JochemsThe D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Netherlands.
Relinde I Y LieverseThe D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Netherlands.
Janita E van TimmerenDepartment of Radiation Oncology, Radboud University Medical Centre, Nijmegen, Netherlands.
Anke WindThe D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Netherlands.
Philippe LambinThe D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this study was to develop and evaluate a proof-of-concept open-source individualized Patient Decision Aid (iPDA) with a group of patients, physicians, and computer scientists. The iPDA was developed based on the International Patient Decision Aid Standards (IPDAS). A previously published questionnaire was adapted and used to test the user-friendliness and content of the iPDA. The questionnaire contained 40 multiple-choice questions, and answers were given on a 5-point Likert Scale (1-5) ranging from "strongly disagree" to "strongly agree." In addition to the questionnaire, semi-structured interviews were conducted with patients. We performed a descriptive analysis of the responses. The iPDA was evaluated by 28 computer scientists, 21 physicians, and 13 patients. The results demonstrate that the iPDA was found valuable by 92% (patients), 96% (computer scientists), and 86% (physicians), while the treatment information was judged useful by 92%, 96%, and 95%, respectively. Additionally, the tool was thought to be motivating for patients to actively engage in their treatment by 92%, 93%, and 91% of the above respondents groups. More multimedia components and less text were suggested by the respondents as ways to improve the tool and user interface. In conclusion, we successfully developed and tested an iPDA for patients with stage I-II Non-Small Cell Lung Cancer (NSCLC).

Indexed as

NSCLCopen-source iPDAparticipative medicinepatient decision aidshared decision-making

Identifiers

PMID38586126
PMCPMC10995236

What Socratic holds

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LicenceCC BY
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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 Socratic graph.