Approaches to prioritising research for clinical trial networks: a scoping review
Bibliographic record
Abstract
BACKGROUND: Prioritisation of clinical trials ensures that the research conducted meets the needs of stakeholders, makes the best use of resources and avoids duplication. The aim of this review was to identify and critically appraise approaches to research prioritisation applicable to clinical trials, to inform best practice guidelines for clinical trial networks and funders. METHODS: A scoping review of English-language published literature and research organisation websites (January 2000 to January 2020) was undertaken to identify primary studies, approaches and criteria for research prioritisation. Data were extracted and tabulated, and a narrative synthesis was employed. RESULTS: Seventy-eight primary studies and 18 websites were included. The majority of research prioritisation occurred in oncology and neurology disciplines. The main reasons for prioritisation were to address a knowledge gap (51 of 78 studies [65%]) and to define patient-important topics (28 studies, [35%]). In addition, research organisations prioritised in order to support their institution's mission, invest strategically, and identify best return on investment. Fifty-seven of 78 (73%) studies used interpretative prioritisation approaches (including Delphi surveys, James Lind Alliance and consensus workshops); six studies used quantitative approaches (8%) such as prospective payback or value of information (VOI) analyses; and 14 studies used blended approaches (18%) such as nominal group technique and Child Health Nutritional Research Initiative. Main criteria for prioritisation included relevance, appropriateness, significance, feasibility and cost-effectiveness. CONCLUSION: Current research prioritisation approaches for groups conducting and funding clinical trials are largely interpretative. There is an opportunity to improve the transparency of prioritisation through the inclusion of quantitative approaches.
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 | 0.511 | 0.756 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.011 |
| Insufficient payload (model declined to judge) | 0.001 | 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".