Scoping review and characteristics of publicly available checklists for assessing clinical trial feasibility
Bibliographic record
Abstract
BACKGROUND: Whether there is sufficient capacity and capability for the successful conduct and delivery of a clinical trial should be assessed by several stakeholders according to transparent and evidence-based criteria during trial planning. For this openly shared, user-tested, and validated tools are necessary. Therefore, we systematically examined the public availability and content of checklists which assess the study-level feasibility in the planning phase of clinical trials. METHODS: In our scoping review we systematically searched Medline, EMBASE, and Google (last search, June 2021). We included all publicly available checklists or tools that assessed study level feasibility of clinical trials, examined their content, and checked whether they were user-tested or validated in any form. Data was analysed and synthesised using conventional content analysis. RESULTS: A total of 10 publicly available checklists from five countries were identified. The checklists included 48 distinct items that were classified according to the following seven different domains of clinical trial feasibility: regulation, review and oversight; participant recruitment; space, material and equipment; financial resources; trial team resources; trial management; and pilot or feasibility studies. None of the available checklists appeared to be user-tested or validated. CONCLUSIONS: Although a number of publicly available checklists to assess the feasibility of clinical trials exist, their reliability and usefulness remain unclear. Openly shared, user-tested, and validated feasibility assessment tools for a better planning of clinical trials are lacking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.484 | 0.911 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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; both teacher heads 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".