Abstract T MP102: Hotline Use for Recruitment Support in Acute Stroke Trials: Lessons learned To Date in Antihypertensive Treatment of Acute Cerebral Hemorrhage (ATACH)-II
Bibliographic record
Abstract
Background: Use of hotline services for clinical support and safe operation of a research trial is common and important. The value of such services has not been objectively assessed within the context of a large acute stroke clinical trial. Methods: The use of three different hotline services have been tested, and cell and Email- options have also been explored for their advantages. US and Non-US sites are provided access to central hotline services, but for efficiency a local network for managing calls is established in each region. All sites may access both data management and the trial PI via hotline services when needed and this is supported by other technologies in addition. A means for supporting overall trial communications in light of these interactions has been developed and valuable insights are gained. Results: Data gathered from 211 calls logged at the Clinical Coordinating Center through the course of the ATACH-II trial have been summarized: Total Calls Reviewed: 211; Year 2 of trial: 92 calls, Year 3 of trial: 100 calls. Calls received midnight to 8 AM: 19 (9.0%), 8 AM - 5 PM 130 (61.6 %) 5 PM - 12:00 PM 65 (30.8%). Issues Resolved in < 5 min 143/211 (67.8%) Taking > 30 min to resolution 51/211 (24.2%). Purpose of calls: Eligibility 89 (42.2%). protocol compliance including drug management: 77 (36.5%%), randomization/emergency randomization: 16 (7.6%), protocol deviation:13 (6.2%), technological difficulties: 12 (5.7%), AE/SAE: 9 (4.3%), Subject enrollments directly associated with calls: 57 (20.8% of domestic subjects); excluded candidates directly associated with calls: 46 (% not available). Conclusions: In an international trial requiring rapid enrollment of subjects with intracerebral hemorrhage, the role of direct support via a hotline and other immediate communications means has proven to be instrumental in maintaining good protocol compliance and supporting enrollment by site team members .
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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 | 0.051 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 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".