A Qualitative Research for Defining Meaningful Attributes for the Treatment of Inflammatory Bowel Disease from the Patient Perspective
Bibliographic record
Abstract
INTRODUCTION: Crohn's disease (CD) and ulcerative colitis (UC) are chronic, inflammatory bowel diseases (IBD). Each class and type of medication available for the treatment of IBD has distinct characteristics and long-term effects that a patient may consider. We present the results of qualitative research that aimed to develop a descriptive framework that outlines the most relevant disease and/or treatment attributes for IBD treatment decisions and focuses on the patient perspective. METHODS: This research employed a three-step approach: a literature review to identify a broad list of attributes, a focus group meeting including patients and clinicians to assess the relevance of the attributes, and two rounds of voting to name and define each attribute. The literature review was used to develop the initial list of attributes. Although the same attributes were defined for both UC and CD, the relative importance of each attribute to UC or CD was considered. The list of attributes was discussed and evaluated in the focus group meeting, which included eight patient representatives and nine gastroenterologists. Using feedback elicited from the focus group meeting, the research team developed a draft of the descriptive framework that grouped the attributes into domain subsets. All members of the focus group participated in two subsequent rounds of structured, online voting, which was used to refine the wording to name and define each attribute. Additionally, participants ranked all the attributes included in the descriptive framework to suggest which attributes were less relevant and could be omitted. RESULTS: Among 574 publications retrieved from the databases and registries, we identified 32 eligible publications, and an initial list of attributes was developed. This list was refined during the focus group meeting, resulting in a draft descriptive framework of attributes within subsets of domains. The final descriptive framework was developed based on structured rounds of online voting to further refine attribute names and definitions. In the final descriptive framework, a total of ten attributes were identified: abdominal pain, other disease-related pain, bowel urgency, fatigue, risk of cancer and serious infections within the next 10 years, risk of mild to moderate complications, aesthetic complications related to treatment, emotional status, sexual life, and social life and relationships. These attributes were distributed across three domains: efficacy, complications and risk, and health-related quality of life. CONCLUSIONS: Through the identification of the ten most relevant attributes that influence patient decision making for IBD treatments, we developed a descriptive framework that should be considered by physicians when discussing IBD treatment options with their patients. The results of our qualitative research may also be helpful for the development of future IBD clinical studies and quantitative research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.148 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.016 | 0.029 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".