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S972 The Real World Global Use of Patient-Reported Outcomes (PROs) for the Care of Patients With IBD

2022· article· en· W4316077317 on OpenAlexaffabout
Jamie Horrigan, Édouard Louis, Antonino Spinelli, Simon Travis, Bjørn Moum, Jessica K. Salwen‐Deremer, Jonas Halfvarson, Remo Panaccione, Marla C. Dubinsky, Pia Munkholm, Corey A. Siegel

Bibliographic record

VenueThe American Journal of Gastroenterology · 2022
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineClinical PracticeQuality of life (healthcare)Inflammatory bowel diseaseFamily medicineMEDLINEDiseaseCross-sectional studyMental healthHealth careNursingInternal medicinePsychiatryPathology

Abstract

fetched live from OpenAlex

Introduction: Many patient-reported outcomes (PROs) have been developed for inflammatory bowel disease (IBD), often for research, without clear recommendations for clinical use. PROs differ from physician-reported disease activity indices; they assess patients’ perceptions of their symptoms, functional status, mental health, and quality of life, among other areas. The use of PROs and their utility in clinical practice is unknown. Thus, we sought to investigate the current global use and barriers to using PROs in clinical practice for IBD. Methods: A cross-sectional survey was performed. Members of the International Organization for the Study of Inflammatory Bowel Disease (IOIBD) were invited to participate and invite regional colleagues. Results: There were 194 respondents, including adult/pediatric gastroenterologists, advanced-practice providers, and colorectal surgeons from 5 continents. The majority (80%) use PROs in clinical practice, 65% found value in routine use, and 50% indicated that PROs influenced patient management. 31 different PROs for IBD were reportedly used in clinical practice. For providers who never use PROs, the most significant barriers were not being familiar with PROs (53%), not knowing how to incorporate the results of PROs into clinical practice (33%), lack of integration into the electronic medical record (EMR) (28%), and time constraints (20%). There was no significant difference in volume of IBD patients seen per week or time spent during a follow up visit between providers who use and do not use PROs. Most participants (91%) agreed that it would be beneficial to have an accepted set of PROs that were consistently used. Suggested PRO tools are listed in Table. The majority (60%) thought that there should be some cultural differences in PROs used globally but that the PROs for IBD should be consistent around the world. Conclusion: PROs are used frequently in clinical practice with wide variation in which PROs are used and how they influence patient management. Education around how to use and interpret an accepted set of PRO tools that are integrated into the EMR would decrease barriers for use and could allow for global harmonization. Patient perceptions of PROs for IBD is being explored and will further inform this process. Table 1. - Suggested patient-reported outcome (PRO) tools to be used in clinical practice for the care of patients with IBD Patient-Reported Outcome (PRO) Tool Proportion of Providers Recommending each PRO Tool (%) PRO2 or PRO3 15.4 Simple clinical colitis activity index (SCCAI) 14.9 Patient-Reported Harvey-Bradshaw Index (patient-reported HBI) 14.6 Survey Index CDAI 10.8 Short IBDQ 10.3 IBD Disk 7.3 IBD Control 4.1 Facit-Fatigue Scale 4.1 Other 3.8 Short Health Scale 3.2 EQ-5D-5L 2.7 General Psychological Well-Being Score (GPP) 2.4 Manitoba Inflammatory Bowel Disease Index 2.4 Work Productivity & Activity Impairment Questionnaire (WPAI) 1.9 PROMIS-10 0.01 ICHOM Standard Set 0.01 * Providers were allowed to respond to more than 1 PRO tool

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)0.009
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.035
GPT teacher head0.360
Teacher spread0.325 · 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 designObservational
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
Published2022
Admission routes2
Has abstractyes

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