An international Delphi survey and consensus meeting to define the core outcome set for trigeminal neuralgia clinical trials
Bibliographic record
Abstract
BACKGROUND: Trigeminal neuralgia (TN) is an excruciating unilateral facial pain, which negatively affects patient's quality of life. Historically, it has been difficult to compare treatment efficacy due to the lack of standardized outcomes. In addition, patients' perspective has seldomly been acknowledged. The aim of this study was to reach consensus on what outcomes of treatment are important to different TN stakeholders (patients, clinicians and researchers), to identify the TN Core Outcome Set (TRINCOS). METHODS: A list of outcomes identified through a systematic review and focus group work was used to develop the survey questionnaire. A three-round Delphi was conducted. Participants were asked to score the outcomes on scale from 1 to 9 (1-3 not important;4-6 important but not critical;7-9 critical). Outcomes scored as critical by ≥70% and not important by <15% were retained, and those for which no consensus was reached were discussed at a consensus meeting. RESULTS: Of the 70 participants who completed the Delphi, 26 were patients, 38 were clinicians and six were researchers. Of the 40 outcomes presented, 17 were scored as critical and no consensus was met for 23 outcomes. Agreement was reached during a consensus meeting on 10 outcomes across six domains (pain, side effects, social impact, quality of life, global improvement, and satisfaction with treatment). CONCLUSION: Implementation of TRINCOS in future clinical trials will improve homogeneity of studies' results, reduce the redundancy in the outcome assessment and effectively allow comparison of different treatments to better inform researchers, clinicians and most importantly patients, about the efficacy of the different treatments. SIGNIFICANCE: Implementation of a 10-item core outcome set in trigeminal neuralgia will improve comparability between studies allowing patients to have faster access to better treatments.
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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 | 0.067 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".