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Record W4242589156 · doi:10.1093/ibd/zaa010.133

P003 INFLAMMATORY BOWEL DISEASE PATIENTS’ PERSPECTIVES OF CLINICAL TRIALS

2020· article· en· W4242589156 on OpenAlexaboutno aff
David T. Rubin, Laurent Peyrin‐Biroulet, Walter Reinisch, Swati Tole, Laura A. Sullivan, K T Park, Miguel Regueiro

Bibliographic record

VenueInflammatory Bowel Diseases · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineClinical trialInflammatory bowel diseaseFamily medicineDiseaseUlcerative colitisCrohn's diseaseHealth careTelephone interviewInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Despite recent progress in treatment for inflammatory bowel diseases (IBD), there is a need for therapies with long-term efficacy and improved safety. Clinical trials in IBD face challenges with patient recruitment because of study designs, competitive or overlapping trials, and a limited number of eligible patients. We sought to better understand patients’ motivations, awareness of, and experience with IBD clinical trials. Methods We conducted an international survey of adult patients with IBD consisting of 2 components. The quantitative component, a 15-minute online survey, was completed by all patients. A qualitative component, a 30-minute telephone interview, was completed by a subset of patients from the United States (US). All percentages indicate results from the online survey. Results 226 patients (mean age, 41.9 y) completed the online survey. Survey respondents included patients with ulcerative colitis (52%) and Crohn’s disease (48%) from the US (n=100, 21 of whom underwent a phone interview), Brazil (n=26), Canada (n=25), France (n=25), Germany (n=25), and Spain (n=25). Ninety-six percent of respondents reported at least a basic understanding of clinical trials, and 34 (15%) were current or past clinical trial participants. Patients reported learning about trials through 1 or more sources (could select as many as applied): health care providers (42%), pharmaceutical manufacturer websites (31%), social media (30%), online support groups (28%), and foundations (18%-23%). In the survey, patients rated conversations with health care providers most helpful, but patients who were interviewed revealed that most physicians often do not initiate conversations about clinical trials, and patients typically do not ask. Primary motivators for trial participation (rated from “does not encourage me at all” to “encourages me very much”) included altruistic goals of advancing medicine (67%), potentially mitigating risks of uncontrolled IBD such as colon cancer (59%), and access to treatment options that could improve quality of life (59%) or would otherwise be unaffordable (52%). Major barriers to participation (rated from “does not discourage me at all” to “discourages me very much”) included invasive screening and monitoring (35%), concern over receiving placebo (35%), or suboptimal treatment (33%), and concerns about posttrial access to study medication (27%). The majority (68%) reported that being in a clinical trial means being a “guinea pig” for an experimental treatment. Conclusion Opportunities to improve patients’ clinical trial experience in IBD include better communication with health care providers and improved patient education about clinical trial design and ethics. Ultimately, a better understanding of the patient perspective will be important for more informed patients and potentially higher recruitment and enrollment.

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)0.013
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.055
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0070.003
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0150.001

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 head0.031
GPT teacher head0.318
Teacher spread0.287 · 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 designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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