The trials methodological research agenda: results from a priority setting exercise
Bibliographic record
Abstract
BACKGROUND: Research into the methods used in the design, conduct, analysis, and reporting of clinical trials is essential to ensure that effective methods are available and that clinical decisions made using results from trials are based on the best available evidence, which is reliable and robust. METHODS: An on-line Delphi survey of 48 UK Clinical Research Collaboration registered Clinical Trials Units (CTUs) was undertaken. During round one, CTU Directors were asked to identify important topics that require methodological research. During round two, their opinion about the level of importance of each topic was recorded, and during round three, they were asked to review the group's average opinion and revise their previous opinion if appropriate. Direct reminders were sent to maximise the number of responses at each round. Results are summarised using descriptive methods. RESULTS: Forty one (85%) CTU Directors responded to at least one round of the Delphi process: 25 (52%) responded in round one, 32 (67%) responded in round two, 24 (50%) responded in round three. There were only 12 (25%) who responded to all three rounds and 18 (38%) who responded to both rounds two and three. Consensus was achieved amongst CTU Directors that the top three priorities for trials methodological research were 'Research into methods to boost recruitment in trials' (considered the highest priority), 'Methods to minimise attrition' and 'Choosing appropriate outcomes to measure'. Fifty other topics were included in the list of priorities and consensus was reached that two topics, 'Radiotherapy study designs' and 'Low carbon trials', were not priorities. CONCLUSIONS: This priority setting exercise has identified the research topics felt to be most important to the key stakeholder group of Directors of UKCRC registered CTUs. The use of robust methodology to identify these priorities will help ensure that this work informs the trials methodological research agenda, with a focus on topics that will have most impact and relevance.
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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 | 0.521 | 0.611 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".