Abstract TP30: Enrolling Patients During Covid-19: Lessons From The Timeless Clinical Trial
Bibliographic record
Abstract
The COVID-19 pandemic negatively impacted clinical trial enrollment worldwide, and patient enrollment continues to lag prepandemic levels. The TIMELESS trial (NCT03785678) is a Phase III study to determine whether tenecteplase treatment increases the proportion of good clinical outcomes in patients with acute ischemic stroke who present 4.5-24 hours after symptom onset. The trial has successfully enrolled patients at >90 sites in the US and Canada despite the ongoing pandemic, associated hospital restrictions, and inherent challenges of enrolling patients in an acute stroke trial. The highest-enrolling study sites identified several barriers to patient enrollment and ways to turn obstacles into opportunities to improve the enrollment process. Common themes in patient enrollment failure included insufficient time to complete the consent process, inadequate coordination between departments, and logistic challenges due to hospital restrictions. Solutions included using telemedicine and electronic consent software to rapidly identify patients and provide sufficient time to complete the informed consent process. Software alerts for new stroke cases and HIPAA-compliant messaging applications ensured rapid activation of all staff needed for patient enrollment. Accessible drug storage for rapid dispensing and a comprehensive “step-forward randomization” workflow reduced or eliminated patient enrollment delays caused by study drug retrieval, preparation, and administration. Well-defined roles for the research teams, combined with training and mock enrollment drills, kept research teams engaged and working efficiently within a limited enrollment window. Clear enrollment workflows ensured coordinated responses from multiple departments and could be adapted to changing hospital COVID-19 restrictions. These challenges, while exacerbated by COVID-19, are relevant beyond the pandemic and acute stroke trials. The lessons and experiences of successful sites show that successful clinical trial enrollment requires clear channels of communication, collaboration with nonresearch stakeholders, and an engaged research team with well-defined roles who are committed to integrating research plans into each site’s clinical workflows.
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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 | 0.130 | 0.298 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".