Clinical research activity in periodontal medicine: a systematic mapping of trial registers
Bibliographic record
Abstract
AIM: The primary aim of the study was to systematically map registration records on periodontal medicine in clinical trial registers. The secondary aim was to assess the evolution of periodontal medicine in clinical periodontal research as a whole. MATERIAL AND METHODS: We searched all registration records related to periodontology in the World Health Organization International Clinical Trials Registry Platform. For registration records classified in the field of periodontal medicine, we assigned the 2015 MeSH(®) term for the most precisely corresponding systemic condition. RESULTS: Fifty-seven systemic conditions have been hypothesized to be linked with periodontal diseases, covering nearly 2% of the diseases indexed in MeSH. In addition to diabetes, cardiovascular disease or preterm birth, other systemic conditions have been the subject of registration records, such as anaemia, liver diseases, dyspepsia or ankylosing spondylitis. A trend towards increasing diversification of systemic conditions has appeared over time. About a third of registration records in clinical periodontal research deals with periodontal medicine. CONCLUSIONS: Periodontal medicine now constitutes an important part of clinical periodontal research. Research activity in periodontal medicine has grown continuously since the early 2000s, and exploration of registers gives a useful up-to-date snapshot of this constantly evolving field of research.
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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 | 0.050 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.035 | 0.047 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".