Silicone toothbrushes: A scoping review of an underutilized tool in global oral health
Bibliographic record
Abstract
Oral diseases are the most prevalent non-communicable diseases worldwide, affecting 3.5 billion people, with a disproportionate impact on those living in low- and middle-income countries. Despite being largely preventable through proper oral hygiene, current oral health promotion strategies rely heavily on plastic and nylon toothbrushes, which present both environmental and accessibility challenges. In response to the growing need for sustainable and affordable preventive oral health solutions, there has been increasing interest in alternatives to conventional toothbrushes. This scoping review aimed to summarize the global literature on silicone toothbrushes, an underutilized tool in preventive oral care. A systematic search of five databases, supplemented by reference screening, identified ten English-language studies investigating silicone toothbrushes. Findings suggest that silicone toothbrushes are effective in plaque removal, have a lower risk of gingival trauma, are well-suited for specific populations, and perform better in environmental impact assessments. This review also demonstrated that silicone toothbrushes remain under-researched and underutilized, highlighting the need for further high-quality studies to evaluate their effectiveness, safety, and broader implementation.
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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".