Bibliographic record
Abstract
Background and aims: Consent in randomized controlled trials (RCTs) can be challenging if immediate intervention is required or if the parents are unable to consider participation because of emotional stress. Aims: To describe the consent models and consent rates in pediatric critical care RCTs. Methods: We included published English-language RCTs from the Evidence in Pediatric Intensive Care database (epicc.mcmaster.ca) of RCTs administering any intervention to children in a pediatric critical care unit. We excluded trials conducted in pre-term infants and cross-over trials. Results: We included 243 RCTs (1986 to 2013) from 32 different countries. 7 (2.9%) of trials reported the use of any approach other than written consent from the child or their parent or guardian (2 waived consent, 2 deferred consent, 2 verbal and 1 sought consent from one group) and 13 (5.3%) reported that they sought assent from the child. 20 (8.2%) reported consent was obtained prior to PICU admission. 74 (30.6%) of the RCTs reported a consent rate. The median (interquartile range) consent rate was 90% (72%, 97%). Using linear regression the of year of publication, commercial funding, prophylactic or pharmaceutical interventions, and pre-PICU consent were not independently associated with increased consent rate. Only 4 (1.6%) reported some characteristics of those who did not consent. Conclusions: Alternative approaches such as deferred and waived consent were used in few studies. There are opportunities to improve the quality of reporting; very few trials reported the information necessary to determine the representativeness of those that consented to participate.
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 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 | 0.018 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.515 | 0.264 |
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".