Evidence map›Paper›PMID 38953019›Full record

ArticlePatient preference and adherence2024

Comparing Methods for Identifying Post-Market Patient Preferences at the Point of Decision-Making: Insights from Patients with Chronic Pain Considering a Spinal Cord Stimulator Device.

Elizabeth H Golembiewski, Montserrat Leon-Garcia, Derek Loy Gravholt, Juan P Brito, Erica S Spatz, Markus A Bendel, Victor M Montori, Andrea P Maraboto, Sandra A Hartasanchez, Ian G Hargraves

Abstract read
In one paragraph

Article in Patient preference and adherence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Elizabeth H GolembiewskiKnowledge and Evaluation (KER) Unit, Mayo Clinic, Rochester, MN, USA.ORCID 0000-0003-1015-3935
Montserrat Leon-GarciaKnowledge and Evaluation (KER) Unit, Mayo Clinic, Rochester, MN, USA.ORCID 0000-0001-8567-4685
Derek Loy GravholtKnowledge and Evaluation (KER) Unit, Mayo Clinic, Rochester, MN, USA.ORCID 0000-0002-2536-8157
Juan P BritoKnowledge and Evaluation (KER) Unit, Mayo Clinic, Rochester, MN, USA.ORCID 0000-0003-1840-1745
Erica S SpatzDivision of Cardiovascular Medicine, School of Medicine, Yale University, New Haven, CT, USA.ORCID 0000-0002-1557-7713
Markus A BendelDivision of Pain Medicine, Mayo Clinic, Rochester, MN, USA.ORCID 0000-0002-3590-2851
Victor M MontoriKnowledge and Evaluation (KER) Unit, Mayo Clinic, Rochester, MN, USA.ORCID 0000-0003-0595-2898
Andrea P MarabotoKnowledge and Evaluation (KER) Unit, Mayo Clinic, Rochester, MN, USA.ORCID 0000-0001-6848-5831
Sandra A HartasanchezKnowledge and Evaluation (KER) Unit, Mayo Clinic, Rochester, MN, USA.ORCID 0000-0001-8957-9554
Ian G HargravesKnowledge and Evaluation (KER) Unit, Mayo Clinic, Rochester, MN, USA.ORCID 0000-0002-1836-3622

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To compare three methods for identifying patient preferences (MIPPs) at the point of decision-making: analysis of video-recorded patient-clinician encounters, post-encounter interviews, and post-encounter surveys. Patients and Methods: For the decision of whether to use a spinal cord stimulator device (SCS), a video coding scheme, interview guide, and patient survey were iteratively developed with 30 SCS decision-making encounters in a tertiary academic medical center pain clinic. Burke's grammar of motives was used to classify the attributed source or justification for a potential preference for each preference block. To compare the MIPPs, 13 patients' encounters with their clinician were video recorded and subsequently analyzed by 4 coders using the final video coding scheme. Six of these patients were interviewed, and 7 surveyed, immediately following their encounters. Results: For videos, an average of 66 (range 33-106) sets of utterances potentially indicating a patient preference (a preference block), surveys 33 (range 32-34), and interviews 25 (range 18-30) were identified. Thirty-eight unique themes (75 subthemes), each a preference topic, were identified from videos, surveys 19 themes (12 subthemes), and interviews 39 themes (54 subthemes). The proportion of preference blocks that were judged as expressing a preference that was clearly important to the patient or affected their decision was highest for interviews (72.8%), surveys (68.0%), and videos (27.0%). Videos mostly attributed preferences to the patient's situation (scene) (65%); interviews, the act of receiving or living with SCS (43%); surveys, the purpose of SCS (40%). Conclusion: MIPPs vary in the type of preferences identified and the clarity of expressed preferences in their data sets. The choice of which MIPP to use depends on projects' goals and resources, recognizing that the choice of MIPP may affect which preferences are found.

Indexed as

decision makingpatient preferencespreference elicitationpreference identificationregulatory

Identifiers

PMID38953019
PMCPMC11215661

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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.