HEROIC Trials to Answer Pragmatic Questions for Hospitalized Children
Bibliographic record
Abstract
Although the number of randomized controlled trials (RCTs) published each year involving adult populations is steadily rising, the annual number of RCTs published involving pediatric populations has not changed since 2005. Barriers to the broader utilization of RCTs in pediatrics include a lower prevalence of disease, less available funding, and more complicated regulatory requirements. Although child health researchers have been successful in overcoming these barriers for isolated diseases such as pediatric cancer, common pediatric diseases are underrepresented in RCTs relative to their burden. This article proposes a strategy called High-Efficiency RandOmIzed Controlled (HEROIC) trials to increase RCTs focused on common diseases among hospitalized children. HEROIC trials are multicenter RCTs that pursue the rapid, low-cost accumulation of study participants with minimal burden for individual sites. Five key strategies distinguish HEROIC trials: (1) dispersed low-volume recruitment, in which a large number of sites (50-150 hospitals) enroll a small number of participants per site (2-10 participants per site), (2) incentivizing site leads with authorship, training, education credits, and modest financial support, (3) a focus on pragmatic questions that examine simple, widely used interventions, (4) the use of a single institutional review board, integrated consent, and other efficient solutions to regulatory requirements, and (5) scaling the HEROIC trial strategy to accomplish multiple trials simultaneously. HEROIC trials can boost RCT feasibility and volume to answer fundamental clinical questions and improve care for hospitalized children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".