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Record W4212801580 · doi:10.1093/jcag/gwab049.103

A104 PATIENT AND PUBLIC INVOLVEMENT (PPIN) IN IBD RESEARCH - A SCOPING REVIEW

2022· review· en· W4212801580 on OpenAlexaff
Karam Elsolh, E Neary, Samir Seleq, Nikko Gimpaya, Michael A. Scaffidi, Rishad Khan, Samir C. Grover

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2022
Typereview
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of TorontoQueen's UniversitySt. Michael's Hospital
Fundersnot available
KeywordsMedicineQuality of life (healthcare)MEDLINEOutcomes researchFamily medicineAlternative medicineNursingPolitical sciencePathology

Abstract

fetched live from OpenAlex

Abstract Background Over the past 10 years, interest in patient and public involvement (PPIn) in research has grown. Several arguments support the engagement of patients as partners in the research process. Patients with lived experience of a condition can offer their knowledge to study design as experience-based experts, helping researchers incorporate patient-pertinent outcomes. PPIn has also been shown to boost patient enrolment and retention in clinical trials. Benefits, challenges, and best practices of PPIn have been examined in other fields. However, to date, no study has examined PPIn in inflammatory bowel disease (IBD) research. Many factors amenable to research involvement may impact IBD patients’ quality of life, including disease morbidity, complications, and efficacy/side effects of therapy. Aims This review aims to characterize methods of PPIn in IBD research and highlight themes relating to best practices, benefits, and challenges. Methods We ran a systematic search on MEDLINE, EMBASE, and Cochrane for all IBD research studies in which IBD patients were involved in the research process. PPIn included but was not limited to patient input in one of the following 3 stages: Study Design (prioritization of research topics, outcome selection, study tool development), Study Execution (recruitment, data collection & analysis), and Dissemination of Research. After abstract and full-text screening, 14 studies were selected. Results Patients were recruited for PPIn through IBD and patient organizations (7/14), outpatient clinics (4/14), tertiary care sites (2/14), and pre-existing patient advisory groups (1/14). The majority of studies (11/14) engaged patients in the development of study materials, which included a physical activity intervention for stoma patients, an IBD pregnancy decision aid, and a quality of life questionnaire. Two studies interviewed patients to determine comprehensibility of survey items and guide revisions. One study involved patients in data analysis and manuscript development. Most consultations were open-ended, including focus groups (8/14) and semi-structured interviews (3/14). According to study authors, PPIn helps guide IBD research priorities by focusing on patient-relevant issues. Authors also cited the role of PPIn in designing patient-friendly study tools. One challenge reported by 2 studies was that PPIn requires patients to have access to high-quality information and requires a significant time commitment, which may contribute to demographic biases. Conclusions The majority of IBD studies engaged patients in an open-ended format and were engaged in study design, particularly in developing study materials. Authors recommend continuous involvement of patients throughout the research process to address their research priorities. Funding Agencies None

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.066
metaresearch head score (Gemma)0.175
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.066
Threshold uncertainty score0.346

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0660.175
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0260.028
Science and technology studies0.0030.004
Scholarly communication0.0100.011
Open science0.0020.006
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.372
GPT teacher head0.480
Teacher spread0.108 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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