The Impact of Nigella sativa on Oral Health: A Scoping Review
Bibliographic record
Abstract
Introduction: Nigella sativa (NS) has been shown to improve periodontal health by reducing alveolar bone resorption and lowering periodontal indices and subgingival bacterial counts. However, public health and safety concerns are emerging, and there have been a few reported adverse effects associated with the use of NS. Thus, the current study aims to review clinical studies on the effectiveness NS for oral health conditions. Methods: The databases Pubmed and Google Scholar were used to search the literature; the year of publication was not restricted in the search. Studies conducted in animal models in laboratories, as well as those involving an intervention of NS in combination with other herbs, were excluded. Results: A total of thirteen human clinical studies that used NS as an intervention for treating different types of oral health conditions were included in this review. Improvement in clinical parameters was reported in all the included studies, although the statistical significance varied. Conclusion: Although the studies show that NS is beneficial in the treatment and management of oral health conditions, there is still a need for more rigorous research in this area, particularly in the application of NS in real-world clinical settings.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".