The Particulars of Applying Odontoprotectors at Different Stages of Therapeutic Process of Periodontal Diseases (A Scoping Review)
Bibliographic record
Abstract
Rational use of drugs underlies the development of a treatment strategy. In particular, it is important in dental practice to properly select odontoprotectors for the prevention, treatment and maintenance therapy of periodontal diseases. A methodological approach based on the Arskey & O’Malley’s framework was applied to analyse the state of knowledge and previous studies on the use of odontoprotector drug group at different stages of the therapeutic process of periodontal disease. Of the 6 initial scientific databases, the research was conducted in 3 databases that best met the specified search conditions: Google (Google Scholar); PubMed; Wiley InterScience (The Cochrane Library). The literature was selected for the last 5 years (2016-2021). A total of 492 scientific papers were analysed. It is established that the available scientific information is divided into 4 main areas: the use of herbal remedies and folk remedies; antibiotic therapy in dental practice; prospects for the use of nanotechnology in dentistry; results of experimental researches and review articles on a particular active pharmaceutical ingredient.
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 | 0.027 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.031 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".