Role of nanotechnology in dentistry:a systematic review
Bibliographic record
Abstract
Nanotechnology involves physical, chemical, and biological properties of nanoscale structures and their components. Nanotechnology is based on the principle of manipulating atoms and molecules one by one, to create usable structures. Many advances in health sciences as well as materials' science, biotechnology, electronic and computer technology, aviation, and space exploration would be possible because of this technology. The objective of the study was to conduct a systematic review of the literature to evaluate the applications of nano-technology in dentistry. Included studies were systematically analyzed based on PRISMA (preferred reporting items for systema tic reviews and meta-analyses), and studies were identified based on PICO (glossary of evidence-based terms, 2007): random clinical trials (RCT), which have been published in last 6 years and in English language only. Electronic database search of PubMed, Medline, Cochrane, and clinicaltrials.gov was performed using MeSH terms: nano technology, nanotechnology in dentistry, and nanotechnology in dental practice. Articles published between 2014-2020 were reviewed, and were included based on inclusion and exclusion criteria. The authors assessed individual study bias by using Cochrane risk of bias tool to find bias risk assessment. Based on this systematic review of literature, it can be concluded that advancement in nanotechnology has significantly influenced dental disease prevention and therapy.
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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 | 0.111 | 0.320 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".