Development and Initial Implementation of a Clinical Monitoring Strategy in a Non-regulated Trial: a research note from the ReStOre II Trial
Bibliographic record
Abstract
<ns3:p> Background Data and Safety Monitoring is integral to quality assurance of clinical trials. Although monitoring is a core legal component of regulated clinical trials, non-regulated trials are not mandated to incorporate monitoring. Consequently, the monitoring process has been underutilised and underreported in this setting. This research report outlines the development and plans for implementing a bespoke Clinical Monitoring Strategy within the ‘ <ns3:italic>Rehabilitation Strategies Following Oesophagogastric and Hepatopancreaticobiliary Cancer (ReStOre II) Trial’</ns3:italic> , a non-regulated trial comparing a 12-week multidisciplinary programme of rehabilitation to standard care in a cohort of 120 cancer survivors. Methods This research note provides a detailed overview of the ReStOre II Clinical Monitoring Strategy and describes the development of the strategy pre and post awarding of the grant. The strategy consists of the establishment and implementation of a comprehensive trial governance structure, inclusive of a Trial Management Group, Trial Steering Committee Meeting, and Independent Data Monitoring Committee. In addition, external trial monitoring by the Clinical Research Facility at St James’s Hospital. Three monitoring visits will be conducted during the trial; i) site initiation visit, ii) interim monitoring visit, and iii) close our visit. Results The Clinical Monitoring Strategy has been finalised and is currently being implemented within the ReStOre II Trial. Two site initiation visits and one interim monitoring visit have been completed to date. Conclusion This research note provides a template for implementation of a Clinical Monitoring Strategy in a non-regulated clinical trial. Registration ReStOre II Trial: https://clinicaltrials.gov/ct2/show/NCT03958019 </ns3:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".