Protocol for the development of an international Core Outcome Set for treatment trials in adults with epilepsy: the EPilepsy outcome Set for Effectiveness Trials Project (EPSET)
Bibliographic record
Abstract
BACKGROUND: A Core Outcome Set (COS) is a standardised list of outcomes that should be reported as a minimum in all clinical trials. In epilepsy, the choice of outcomes varies widely among existing studies, particularly in clinical trials. This diminishes opportunities for informed decision-making, contributes to research waste and is a barrier to integrating findings in systematic reviews and meta-analyses. Furthermore, the outcomes currently being measured may not reflect what is important to people with epilepsy. Therefore, we aim to develop a COS specific to clinical effectiveness research for adults with epilepsy using Delphi consensus methodology. METHODS: The EPSET Study will comprise of three phases and follow the core methodological principles as outlined by the Core Outcome Measures in Effectiveness Trials (COMET) Initiative. Phase 1 will include two focused literature reviews to identify candidate outcomes from the qualitative literature and current outcome measurement practice in phase III and phase IV clinical trials. Phase 2 aims to achieve international consensus to define which outcomes should be measured as a minimum in future trials, using a Delphi process including an online consensus meeting involving key stakeholders. Phase 3 will involve dissemination of the ratified COS to facilitate uptake in future trials and the planning of further research to identify the most appropriate measurement instruments to use to capture the COS in research practice. DISCUSSION: Harmonising outcome measurement across future clinical trials should ensure that the outcomes measured are relevant to patients and health services, and allow for more meaningful results to be obtained. CORE OUTCOME SET REGISTRATION: COMET Initiative as study 118 .
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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 | 0.278 | 0.348 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.090 | 0.029 |
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".