Pragmatic clinical trials to advance research in children and adolescents with eating disorders
Bibliographic record
Abstract
OBJECTIVE: To discuss the utility of pragmatic clinical trials (PCTs) to help advance research in eating disorders (EDs). METHODS: We describe challenges associated with traditional explanatory research trials and examine PCTs as an alternative, including a review of the PRECIS-2 tool. RESULTS: There are many challenges associated with the design and completion of traditional RCTs within the field of EDs. Pragmatic clinical trials are studies that closely align with conditions available in everyday practice and focus on outcomes that are relevant to patients and clinicians. Results of PCTS maximize applicability and generalizability to clinical settings. DISCUSSION: Available therapies established for the treatment of EDs provide remission rates that rarely exceed 50%, implying a need for additional research on new or adjunctive treatments. In addition to a general overview of PCTs, we draw upon published literature and our own experiences involving adjunctive olanzapine for the treatment of children and youth with anorexia nervosa to help highlight challenges associated with randomized controlled trial (RCT) design and implementation, and offer pragmatic suggestions that would allow patients greater choice in treatment trials, while at the same time capturing outcomes that are most likely to advance treatment efforts. CONCLUSIONS: Pragmatic clinical trials provide alternatives to RCT design that can help bolster research in EDs that aims to explore real-world effects of interventions. PUBLIC SIGNIFICANCE: Available therapies established for the treatment of eating disorders (EDs) in children and adolescents provide remission rates that rarely exceed 50%, implying a need for additional research on new or adjunctive treatments. In this article, we discuss the utility of pragmatic trials to help promote research that can help advance knowledge that is relevant to clinical care settings.
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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 | 0.496 | 0.730 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 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".