Bibliographic record
Abstract
Background and aims: An objective of research networks is to foster and execute high-quality clinical research. Aims: We compared randomized controlled trials (RCTs) conducted by pediatric critical care research networks, to non-network trials. Methods: We included all English-language RCTs from the Evidence in Pediatric Intensive Care Collaborative database (epicc.mcmaster.ca), published from the year of the first reported network trial (1999), to October 1013. Trials were considered to be conducted by a research network if specified in the methods. Outcomes of interest were a) productivity, as defined by the size, efficiency and number of trials; b) quality, as determined by the risk of bias; and c) impact, as determined by the journal impact factor (IF) and citation rate. Results: There were a total of 251 RCTs, 11 (4%) of which were conducted by a research network. Compared to non-network trials, research network RCTs were more often multi-centered (100% vs. 15%, p<0.001) successfully funded (100% vs. 52%; p=0.004), and larger (median sample size 152 vs. 50; p= 0.004). Network trials were published in higher impact journals (median IF 11 vs. 3; p=0.003), and cited more frequently; median 9 vs. 2 citations per year; p=0.003. Trials conducted by research networks more often reported being stopped early, 55% vs 12%, p=0.01. Conclusions: While a minority pediatric critical care RCTs are conducted by research networks, they appear to be of higher quality and impact, when compared to non-network trials. Such networks may serve as models for high quality research in pediatric critical care.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.767 | 0.687 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".