Use of a screen log to audit patient recruitment into multiple randomized trials in the intensive care unit
Bibliographic record
Abstract
OBJECTIVE: To develop and evaluate a screen log for monitoring enrollment in multiple randomized clinical trials conducted in a single center. SETTING: University-affiliated 20-bed tertiary care medical-surgical intensive care unit (ICU). PATIENTS: Consecutive ICU patients admitted between April 1995 and March 1997. METHODS: We developed a screen log for multicentered studies conducted in our ICU. Using a multiple-project, unicenter perspective, we evaluated the screen log as a tool for monitoring eligibility and enrollment of patients in four multicentered randomized trials focused on stress ulcer prophylaxis, blood transfusion thresholds, immunotherapy for sepsis and mechanical ventilation strategies. RESULTS: The screen log was used as an instrument to monitor trial execution. We recorded all aspects of study enrollment and created a taxonomy of reasons for nonenrollment into each trial. We calculated enrollment efficiency rates and used these data to develop strategies to maximize accrual. The screen log became a communication tool that fostered research-oriented continuous quality improvement initiatives for the management of concurrently conducted randomized trials in our ICU. CONCLUSIONS: Intensivists participating in several clinical trials may be interested in monitoring and maximizing enrollment when conducting multiple studies and understanding the influence of each trial on enrollment into the others. The unicenter, multiple-project screen log is one tool that may help to achieve these goals.
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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 | 0.357 | 0.649 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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; 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".