Clinical Findings Arising from the Use of Silver Diamine Fluoride to Prevent or Treat Caries Lesions and Dentinal Hypersensitivity: A Data Mining Analysis
Bibliographic record
Abstract
Objective: To summarize data of clinical trials that used silver diamine fluoride (SDF) to prevent and treat caries lesions and dentinal hypersensitivity. Material and Methods: Six electronic databases were searched in May 2022. The concentration of SDF, type of usage (alone/combined), dentition, anterior/posterior teeth, tooth region, dental tissue, number of the treated surfaces, the intervention environment, participants' age, frequency and duration of SDF application, purpose, and outcome were the extracted variables. The type of study, year of publication, authors, journals, and country were also investigated. Results: From 8860 articles, S3 were selected. Most were randomized (n=38), that applied 38% SDF (n=43), alone (n=44), on multiple surfaces (n=44), only in dentin (n=36), of the crown (n=46) of anterior and posterior (n=36) primary teeth (n=39). The studies were preferably carried out outside the clinic (n=3l), only in children (n=33), with reapplication of SDF (n=30), but did not inform the duration of application (n= 19). SDF was most used to treat (n=46) only caries lesions (n=50). They were published between 2001 and 2022, mainly in the Journal of Dentistry (n=10). China (n=19) and Lo E.GM (n=19) were the countries and authors that published the most, respectively. Conclusion: The silver diamine fluoride 38% alone was most used to treat caries lesions in the dentin of the crown of all primary teeth, preferably applied on multiple surfaces, requiring re application, and outside the clinic.
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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.029 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".