Development of an Online Tool for Periodontal Disease Education
Bibliographic record
Abstract
Worldwide research has indicated that 50‐90% of the population suffers from gingivitis, a mild form periodontal disease. Recent studies suggest that periodontitis may be detrimental to oral health and have systemic consequences, such as diabetes and stroke. Recent evidence has shown that patients value computer‐based patient education. The creation of a computer‐based tool could be highly effective in increasing dental knowledge, promoting proper oral hygiene and possibly decreasing periodontal disease rates. An online computer‐based patient education tool was created using Articulate Storyline software with assistance from the Informational Technology Resource Center at Western University. The online tool consists of 4 modules: what is periodontal disease, types of periodontal disease, causes of periodontal disease and prevention of periodontal disease. Module quizzes and a risk assessment section were also developed. Each module consists of relevant information in basic nonscientific terms, pictures and a quiz for information retention. A risk assessment section is then presented at the end in order for patients to assess their risk for developing gum disease. Future work will consider a pilot study to assess patient satisfaction within an outreach dental program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.012 |
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".