What is the purpose of Clinical Trial Monitoring?
Bibliographic record
Abstract
Abstract Background The sources of information on clinical trial monitoring do not give information in an accessible language and do not give detailed guidance. In order to enable communication and to build clinical trial monitoring tools on a strong easily communicated foundation, we identified the need to define monitoring in accessible language. Methods In a three-step process, the material from sources that describe clinical trial monitoring were synthesized into principles of monitoring. A poll re their applicability was run at a UK national academic clinical trials monitoring meeting. Results The process derived 5 key principles of monitoring; keeping participants safe and respecting their rights, having data we can trust, making sure the trial is being run as it was meant to be, improving the way the trial is run, and preventing problems before they happen. Conclusion From the many sources mentioning monitoring of clinical trials, the purpose of monitoring can be summarised simply as 5 principles. These principles, given in accessible language, should form a firm basis for discussion of monitoring of clinical trials.
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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 | 0.651 | 0.807 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.030 | 0.029 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.019 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".