Tooth Whitening with Hydroxyapatite: A Systematic Review
Bibliographic record
Abstract
A steadily increasing public demand for whiter teeth has resulted in the development of new oral care products for home use. Hydroxyapatite (HAP) is a new ingredient to whiten teeth. This systematic review focuses on the evidence of whether HAP can effectively whiten teeth. A systematic search using the PICO approach and PRISMA guidelines was conducted using PubMed, Scopus, Web of Science, SciFinder, and Google Scholar as databases. All study designs (in vitro, in vivo) and publications in foreign language studies were included. Of the 279 study titles that the searches produced, 17 studies met the inclusion criteria. A new "Quality Assessment Tool For In Vitro Studies" (the QUIN Tool) was used to determine the risk of bias of the 13 studies conducted in vitro. Moreover, 12 out of 13 studies had a low risk of bias. The in vivo studies were assigned Cochrane-based GRADE scores. The results in vitro and in vivo were consistent in the direction of showing a statistically significant whitening of enamel. The evidence from in vitro studies is rated overall as having a low risk of bias. The evidence from in vivo clinical trials is supported by modest clinical evidence based on six preliminary clinical trials. It can be concluded that the regular use of hydroxyapatite-containing oral care products effectively whitens teeth, but more clinical trials are required to support the preliminary in vivo evidence.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".