Multistudy Research Operations in the ICU: An Interprofessional Pandemic-Informed Approach
Bibliographic record
Abstract
OBJECTIVES: Proliferation of COVID-19 research underscored the need for improved awareness among investigators, research staff and bedside clinicians of the operational details of clinical studies. The objective was to describe the genesis, goals, participation, procedures, and outcomes of two research operations committees in an academic ICU during the COVID-19 pandemic. DESIGN: Two-phase, single-center multistudy cohort. SETTING: University-affiliated ICU in Hamilton, ON, Canada. PATIENTS: Adult patients in the ICU, medical stepdown unit, or COVID-19 ward. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: An interprofessional COVID Collaborative was convened at the pandemic onset within our department, to proactively coordinate studies, help navigate multiple authentic consent encounters by different research staff, and determine which studies would be suitable for coenrollment. From March 2020 to May 2021, five non-COVID trials continued, two were paused then restarted, and five were launched. Over 15 months, 161 patients were involved in 215 trial enrollments, 110 (51.1%) of which were into a COVID treatment trial. The overall informed consent rate (proportion agreed of those eligible and approached including a priori and deferred consent models) was 83% (215/259). The informed consent rate was lower for COVID-19 trials (110/142, 77.5%) than other trials (105/117, 89.7%; p = 0.01). Patients with COVID-19 were significantly more likely to be coenrolled in two or more studies (29/77, 37.7%) compared with other patients (13/84, 15.5%; p = 0.002). Review items for each new study were collated, refined, and evolved into a modifiable checklist template to set up each study for success. The COVID Collaborative expanded to a more formal Department of Critical Care Research Operations Committee in June 2021, supporting sustainable research operations during and beyond the pandemic. CONCLUSIONS: Structured coordination and increased communication about research operations among diverse research stakeholders cultivated a sense of shared purpose and enhanced the integrity of clinical research operations.
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 | 0.321 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".