P0695 Quantifying the benefit-risk trade-offs adult patients and providers are willing to make when considering advanced therapies for moderate to severe Ulcerative Colitis: A discrete choice experiment
Bibliographic record
Abstract
Abstract Background As advanced therapy options for ulcerative colitis (UC) increase, it is important to understand the benefit-risk trade-offs patients and providers are willing to make to better inform shared decision making. Methods We used a cross-sectional survey with a discrete choice experiment (DCE) design to quantify advanced UC therapy preferences in patients with moderate to severe UC and practicing providers from the US, UK, France, Germany, Italy and Spain. Respondents chose between hypothetical combinations of attributes that were informed by a targeted literature search and formative qualitative research with patients and clinicians. Attributes comprised of time until symptom improvement, probability of remission at one year, probability of corticosteroid (CS)-free remission at one year, annual risk of serious infection, five-year risk of cancer and annual risk of major adverse cardiovascular event (MACE). Relative importance (RI; scaled 0–100%) for each attribute was calculated as the difference in mean preference weights between the most and least preferred level divided by the sum of the differences; for RI, remission attributes were combined. The maximum-acceptable risk (MAR) for each risk attribute, and the simultaneous MAR thresholds (SMARTs) for all risk attributes considered jointly, in exchange for a 10-percentage point increase in probability of remission at one year were estimated. Results For treatment choices, combined remission and CS-free remission at one year was significantly prioritised by patients (N=557; RI 40.1%) and providers (N=500; RI 51.1%), followed by five-year risk of cancer (RI 31.7% and 25.7%, respectively) (Table). Time to symptom improvement was significantly preferred vs annual risk of MACE and serious infection for providers, but not patients (Table). For a 10-percentage point increase in probability of remission at one year, providers had a higher MAR for five-year risk of cancer vs patients (4.0% vs 2.5%); for the other two risk attributes, reported MARs were beyond the DCE-included limits (Figure). For a 10-percentage point increase in the probability of remission at one year when annual risk of serious infection was 1% or 3%, providers were willing to jointly accept higher annual risk of MACE and five-year risk of cancer vs patients (Figure). Conclusion Combined probability of remission and CS-free remission at one year had the strongest influence on treatment choice. Compared with patients, providers on average placed greater importance on benefits and had a higher tolerance of risks, particularly 5-year risk of cancer. These findings highlight the importance of shared clinical decision making. References Pfizer’s generative artificial intelligence tool MAIA was used to assist production of the abstract first draft. Authors reviewed/edited and take responsibility for the content.
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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 | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".