672 Defining Meaningful Attributes for the Treatment of IBD From Patients' Perspective
Bibliographic record
Abstract
INTRODUCTION: Crohn's disease (CD) and ulcerative colitis (UC) are chronic, debilitating inflammatory bowel diseases (IBD). Medications used to treat IBD pts have distinct characteristics. Research into patient preferences indicates values regarding medical treatment may vary depending on clinical disease phenotype. 1–4 We present a descriptive system that includes the most relevant attributes for IBD treatment focusing on the patient perspective. METHODS: A three-step approach was used to develop the descriptive system. First, a literature review was performed and an initial list of attributes was developed and classified within domains and categories. Second, a focus group meeting was conducted with eight patient representatives and nine gastroenterologists. Using feedback elicited from the focus group meeting, the research team constructed an initial draft of the descriptive system, including a subset of domains and attributes. Third, all participants of the focus group meeting participated in two-rounds of structured online interviews. The structured interviews were used to refine the wording used for naming and defining each attribute and the levels of those attributes in the initial descriptive system. RESULTS: We identified 32 eligible publications and a list of 127 attributes grouped into 7 domains (effectiveness, side-effects, health related quality of life [HRQoL], well-being, available evidence, administration/convenience, and other) was developed. This list was discussed in the focus group meeting and a draft of the descriptive system containing 16 relevant attributes was constructed. The same attributes were defined for UC and CD while taking into consideration that the relative weights for each disease may differ. During the first round of interviews, patients ranked all attributes included in the descriptive system and based on the second round of interviews, the final descriptive system containing a total of 3 domains, 10 attributes, and their corresponding levels was developed (Table 1). CONCLUSION: This qualitative research shows which attributes within the domains of efficacy, complications/risk, and HRQoL patients value most when making treatment decisions. We developed a descriptive system that outlines the IBD treatments by means of the 10 most relevant attributes, which should be considered by physicians and nurses when discussing treatment options with a patient. These attributes will be weighted in a future study based on patient preferences.
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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 | 0.027 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".