World Workshop on Oral Medicine VIII: Development of a core outcome set for oral lichen planus: a consensus study
Bibliographic record
Abstract
OBJECTIVE: A core outcome set (COS) is the minimum agreed-on data set required to be measured in interventional trials. To date, there is no COS for oral lichen planus (OLP). This study describes the final consensus project that brought together the results of the previous stages of the project to develop the COS for OLP. STUDY DESIGN: The consensus process followed the Core Outcome Measures in Effectiveness Trials guidelines and involved the agreement of relevant stakeholders, including patients with OLP. Delphi-style clicker sessions were conducted at the World Workshop on Oral Medicine VIII and the 2022 American Academy of Oral Medicine Annual Conference. Attendees were asked to rate the importance of 15 outcome domains previously identified from a systematic review of interventional studies of OLP and a qualitative study of OLP patients. In a subsequent step, a group of OLP patients rated the domains. A further round of interactive consensus led to the final COS. RESULTS: The consensus processes led to a COS of 11 outcome domains to be measured in future trials on OLP. CONCLUSION: The COS developed by consensus will help reduce the heterogeneity of outcomes measured in interventional trials. This will allow future pooling of outcomes and data for meta-analyses. This project showed the effectiveness of a methodology that could be used for future COS development.
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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 | 0.431 | 0.357 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".