Assessing physician preferences on future therapeutic options and diagnostic practices in non-alcoholic steatohepatitis
Bibliographic record
Abstract
BACKGROUND & AIMS: There is currently no data on physician preferences regarding future therapies for non-alcoholic steatohepatitis (NASH); this study explores these preferences and characteristics that are relevant to physician decision-making when choosing a potential therapy for a patient with NASH. The results were compared with those from a similar patient preference survey which was conducted in parallel. METHOD: Initial exploratory 30-minute telephone interviews were conducted to inform the design of a 15-minute quantitative online specialist physicians survey, containing direct questions and a preference survey. This was based on a best-worst scaling (BWS) experiment to assess the relative importance of different treatment characteristics (attributes), followed by several paired comparison questions to understand the preference for 5 hypothetical product profiles. RESULTS: The answers come from 121 physicians from Canada (n = 31), Germany (n = 30), the UK (n = 30) and the USA (n = 30). The primary driving element in NASH treatment decision-making was efficacy (49.23%), defined as "[hypothetical product] impact on liver status" and "[slowing of] progression to cirrhosis". Physicians reported the common use of non-invasive NASH diagnostic tests and 81% reported performing liver biopsy. In 57% of cases, physicians reported that "concerns related to the available diagnostic methods" limit the number of patients with biopsy-confirmed NASH. CONCLUSIONS: This first physician preference study reveals that efficacy will be the main driver for physicians in selecting future NASH drugs. The findings also confirm the widespread use of non-invasive diagnostic tests and the reluctance to perform confirmatory liver biopsy despite guideline recommendations, mainly due to limited therapeutic options and patient refusal. LAY SUMMARY: This study explores physician preferences in relation to future therapies for non-alcoholic steatohepatitis (NASH) and characteristics that are relevant to physician decision-making when choosing a potential therapy for a patient with NASH. The results of a short online survey completed by 121 specialist physicians determined that the primary factor that influences treatment decision-making is efficacy, and that a wide range of non-invasive techniques are used to diagnose NASH, while confirmatory liver biopsy is not performed by all physicians despite guideline recommendations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".