Strategies to Maximize Enrollment in a Prospective Study of Comatose Children in the PICU*
Bibliographic record
Abstract
OBJECTIVES: To analyze barriers to recruitment encountered during a prospective study in the PICU and evaluate strategies implemented to improve recruitment. DESIGN: Prospective observational study of continuous electroencephalogram monitoring in comatose children. SETTING: PICUs at four North American institutions. PATIENTS: Patients with a Glasgow Coma Scale score of less than or equal to 8 for at least an hour. INTERVENTIONS: Four strategies to increase recruitment were sequentially implemented. MEASUREMENTS AND MAIN RESULTS: The baseline enrollment rate was 2.1 subjects/mo, which increased following the single-site introduction of real-time patient screening using an online dashboard (4.5 subjects/mo), deferred consenting (5.2 subjects/mo), and weekend screening (6.1 subjects/mo). However, the subsequent addition of three new study sites was the greatest accelerator of enrollment (21 subjects/mo), representing a 10-fold increase from baseline (p < 0.0001). CONCLUSIONS: Identifying barriers to recruitment and implementing creative strategies to increase recruitment can successfully increase enrollment rates in the challenging ICU environment.
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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 | 0.120 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".