Bioactivity of Dental Restorative Materials: FDI Policy Statement
Bibliographic record
Abstract
The term bioactivity is being increasingly used in medicine and dentistry. Due to its positive connotation, it is frequently utilised for advertising dental restorative materials. However, there is confusion about what the term means, and concerns have been raised about its potential overuse. Therefore, FDI decided to publish a Policy Statement about the bioactivity of dental restorative materials to clarify the term and provide some caveats for its use in advertising. Background information for this Policy Statement was taken from the current literature, mainly from the PubMed database and the internet. Bioactive restorative materials should have beneficial/desired effects. These effects should be local, intended, and nontoxic and should not interfere with a material's principal purpose, namely dental tissue replacement. Three mechanisms for the bioactivity of such materials have been identified: purely biological, mixed biological/chemical, or strictly chemical. Therefore, when the term bioactivity is used in an advertisement or in a description of a dental restorative material, scientific evidence (in vitro or in situ, and preferably in clinical studies) should be provided describing the mechanism of action, the duration of the effect (especially for materials releasing antibacterial substances), and the lack of significant adverse biological side effects (including the development and spread of antimicrobial resistance). Finally, it should be documented that the prime purpose, for instance, to be used to rebuild the form and function of lost tooth substance or lost teeth, is not impaired, as demonstrated by data from in vitro and clinical studies. The use of the term bioactive dental restorative material in material advertisement/information should be restricted to materials that fulfil all the requirements as described in the FDI Policy Statement.
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 | 0.049 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.051 | 0.018 |
| Insufficient payload (model declined to judge) | 0.041 | 0.055 |
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".