Researching periodontitis: challenges and opportunities
Bibliographic record
Abstract
AIM AND METHODS: The evidence-based approach, voted in January 2007 as one of the 15 most important medical advances in the last 166 years, has increasingly shaped medical practice and education. In this paper, we apply the evidence-based approach to evaluate the aetiology of periodontitis; for comparison, we provide a brief description of the evidence-based method applied to the study of cardiovascular disease aetiology. We then discuss the challenges and opportunities to enhance the evidence base for periodontitis aetiology. RESULTS AND CONCLUSION: While evidence for medical treatments has mostly come from clinical trials, evidence for primary prevention in medicine has largely emerged from cohort studies evaluating disease risk factors. The high cost of conducting large cohort studies makes it challenging to fund these investigations, particularly for primary dental outcomes such as periodontitis. Studies of periodontitis outcomes integrated into larger ongoing cohorts provide one way to overcome this problem. Other potential barriers to the conduct of these studies include outcome definition, prevention of bias, data analysis, and the focus on teeth at risk (rather than people at risk) of the outcome. We analyse these questions and provide possible solutions. As many of these issues are generic to dentistry, possible solutions can improve the quality of future studies and the evidence base for primary prevention in dentistry.
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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 | 0.059 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".