Expert Beliefs Regarding Novel Lipid‐Based Approaches to Pediatric Intestinal Failure–Associated Liver Disease
Bibliographic record
Abstract
OBJECTIVE: To determine expert beliefs regarding the probability of intestinal failure-associated liver disease (IFALD) with novel lipid-based approaches (lipid minimization/ω-3 lipids) in managing IFALD to facilitate Bayesian analyses of clinical trials of these therapies. STUDY DESIGN: Structured interviews were conducted using a validated approach to belief elicitation with 60 intestinal failure (IF) experts from across North America. Participants were asked to estimate, in an average population of infants referred for management of IF with early IFALD, the probability of advanced IFALD at 3 months following referral in each of 3 scenarios: (1) conventional lipid, (2) ω-3 lipids, and (3) lipid minimization. Probability distributions of the risk of advanced IFALD with each strategy were developed. Distributions of the elicited treatment effect for the novel approaches, relative to conventional lipid, were calculated. RESULTS: Median duration of experience of participants managing patients with IF was 8.5 (range, 2-35) years. The median probability of advanced IFALD using conventional lipid was 32.5%; ω-3 lipids, 17.5%; and lipid minimization, 13%. The median of the elicited treatment effects relative to conventional lipid was a relative risk of 0.53 for the ω-3 lipid and 0.45 for lipid minimization. CONCLUSIONS: There was consistent expert opinion that the novel lipid-based approaches are superior to conventional therapy, with similar estimates of treatment efficacy for the 2 approaches. The distributions of the elicited treatment effects can be used as prior distributions in Bayesian analyses of clinical trials of these novel strategies.
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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 | 0.030 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".