World Workshop on Oral Medicine VIII: Development of a core outcome set for oral lichen planus: a systematic review of outcome domains
Bibliographic record
Abstract
OBJECTIVE: There is a lack of consensus regarding clinician- and patient-reported oral lichen planus (OLP) outcomes. The World Workshop on Oral Medicine Outcomes Initiative for the Direction of Research (WONDER) Project aims to develop a core outcome set (COS) for OLP, which would inform the design of clinical trials and, importantly, facilitate meta-analysis, leading to the establishment of more robust evidence for the management of this condition and hence improved patient care. STUDY DESIGN: Ovid MEDLINE, Embase, CINAHL, CENTRAL, and Clinicaltrials.gov were searched for interventional studies (randomized controlled trials, controlled clinical trials, and case series including ≥5 participants) on OLP and oral lichenoid reactions published between January 2001 and March 2022 without language restriction. All reported primary and secondary outcomes were extracted. RESULTS: The searches yielded 9,135 records, and 291 studies were included after applying the inclusion criteria. A total of 422 outcomes were identified. These were then grouped based on semantic similarity, condensing the list to 69 outcomes. The most frequently measured outcomes were pain (51.9%), clinical grading of the lesions (29.6%), lesion size/extension/area (27.5%), and adverse events (17.5%). CONCLUSION: As a first step in developing a COS for OLP, we summarized the outcomes that have been used in interventional studies over the past 2 decades, which are numerous and heterogeneous.
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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 | 0.040 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.018 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".