Improving the quality conduct and efficiency of clinical trials with training: Recommendations for preparedness and qualification of investigators and delegates
Bibliographic record
Abstract
The Clinical Trials Transformation Initiative (CTTI) Investigator Qualification Project addresses the need for a more efficient and effective means of identifying qualified clinical investigators and delegates. Selection of investigators and delegates who are qualified by training and experience to conduct clinical trials is essential to safeguarding protections for study participants and ensuring data quality and integrity. Sponsors generally document investigator qualification through training on the principles of good clinical practice (GCP), as defined by the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH), adopted by regulatory authorities in the United States, Japan and the European Union. Although these GCP principles provide an important foundation for promoting the conduct of quality clinical trials, the industry standard "one-size-fits-all" GCP training may not fully prepare investigators and delegates for conducting quality clinical trials. Routine GCP training alone may not be sufficient to prepare an inexperienced member of a site team, while repeating such training is unlikely to enhance the qualifications of an experienced researcher. The CTTI project team used findings from qualitative research activities, as well as input from an expert meeting with multiple stakeholders, to identify gaps and redundancies in the current training of investigators and their delegates and recommend practical, action-based solutions. CTTI provides recommendations on how to implement a more efficient and effective means of preparedness and qualification of investigators and delegates, determining whether a site team is a good fit for a particular protocol, and improving the quality of clinical trial conduct.
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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 | 0.689 | 0.764 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.026 | 0.028 |
| Open science | 0.020 | 0.021 |
| Research integrity | 0.025 | 0.034 |
| Insufficient payload (model declined to judge) | 0.011 | 0.013 |
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".