Evidence map›Paper›PMID 42183367›Full record

ArticleResearch square2026

Encounter tool for Shared Decision Making about Adjuvant Treatment of Lung Cancer: Randomized Clinical Trial.

Satya Sai Sri Bandi, Megan E Branda, Maria Mateo Chavez, Shubhangi Bagewadi, Aurora S Norman, Victor Montori, Michael R Gionfriddo, Iris G Hargraves, Kasey Boehmer, Angela Stephen and 4 more

Registry-linked trialAbstract readPreprint
In one paragraph

Article in Research square, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06122064 (Shared Decision-Making Encounter Tool for Adjuvant Treatment of Lung Cancer), which is not on this map. Not yet cited in PubMed.

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

NCT06122064 narecruitingnot on this map

Shared Decision-Making Encounter Tool for Adjuvant Treatment of Lung Cancer: Randomized Control Trial

TypeinterventionalSponsorMayo ClinicRan2023 to 2029Enrolled100ConditionsLung Non-Small Cell Carcinoma, Stage II Lung Cancer AJCC v8, Stage III Lung Cancer AJCC v8, Stage IV Lung Cancer AJCC v8ArmsBest Practice, Communication Intervention, Electronic Health Record Review, Survey Administration, Video Recording
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

14 authors.

Satya Sai Sri BandiMayo Clinic.
Megan E BrandaMayo Clinic.
Maria Mateo ChavezMayo Clinic.
Shubhangi BagewadiMayo Clinic.
Aurora S NormanMayo Clinic.
Victor MontoriMayo Clinic.
Michael R GionfriddoGeisinger College of Health Sciences.
Iris G HargravesMayo Clinic.
Kasey BoehmerMayo Clinic.
Angela StephenMayo Clinic.
Christi SagenMayo Clinic.
Victor M MontoriMayo Clinic.
Juan P BritoMayo Clinic.
Konstantinos LeventakosMayo Clinic.

Funding

Mayo Clinic Center for Clinical and Translational Science (CCaTS UL1 Supplement - Dr. Timothy Curry)UL1TR002377 · NCATS · MAYO CLINIC ROCHESTER · PI VESNA D GAROVIC · 2017 to 2026
$78.4M
NCATS NIH HHS UL1 TR002377
6 · The paper itself

Abstract

Background: Lung cancer is the leading cause of cancer-related death, and the most common cancer diagnosed globally. Approximately 85% of lung cancer cases are non-small cell lung cancer(NSCLC). After initial surgical treatment, adjuvant therapy with conventional or targeted chemotherapy can help optimize survival. These treatments, however, must be tailored to each patients' problematic situation, including cancer characteristics and goals of care.Shared decision making (SDM) is a method of care by which patients and clinicians work together to form plans of care that advance the patient's problematic human situation. This method often requires evaluating alternative options, "trying them on" for fit given their practical demands and favorable and unfavorable effects. Tools used within the encounter can facilitate and support SDM conversations, yet none exist for considering care post-surgical treatment of NSCLC.Therefore, we aimed to evaluate the effectiveness of a novel encounter tool for SDM, NSCLC Choice, compared to usual care at improving SDM and reducing decisional conflict for patients with resected early-stage NSCLC considering adjuvant treatment. Methods: We will conduct a parallel, patient-level randomized trial comparing NSCLC Choice with usual care. Patients will be eligible if they are adults with stage >1B NSCLC eligible for postsurgical adjuvant chemotherapy. Allocation will be central and post-consent. Patients will be blinded to our trial hypothesis, instead of being invited to evaluate an educational intervention. Our primary outcome is SDM quality, which we will assess using participant reported measures (patient SDM-Q-9) obtained immediately post-encounter and an observer measure estimated from video recording of trial encounters in both arms (OPTION12). With resources to conduct a 100-patient trial, we will have >80% power to detect a 12-point medium difference between arms in patient-reported SDM-Q-9. We will also assess the secondary outcomes of knowledge about the disease and the features of treatment options using an ad hoc questionnaire, decisional conflict using the Decisional Conflict Scale, and patient and clinician satisfaction using the metrics used in prior SDM intervention trials. We will also determine encounter length using video recordings. Discussion: In testing NSCLC Choice, we seek to determine the extent to which this intervention can improve care for patients with NSCLC considering adjuvant therapy after surgery. In addition, we expect to determine whether this intervention is satisfactory to participants and feasible in practice. If successful, this study will result in an evidence-based tool for promoting patient-centered conversations around NSCLC and cancer care those fits. Trial Registration: NCT06122064 ClinicalTrials.gov.

Identifiers

PMID42183367
PMCPMC13193129

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Registered trials

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.