What Constitutes an Ideal Dental Restorative Material?
Bibliographic record
Abstract
Intense environmental concerns recently have prompted dentistry to evaluate the performance and environmental impact of existing restoration materials. Doing so entices us to explore the 'what if?' innovation in materials science to create more ideal restorative materials. Articulating a specification for our design and evaluation methods is proving to be more complicated than originally anticipated. Challenges exist not only in specifying how the material should be manipulated and perform clinically but also in understanding and incorporating implications of the skill of the operator placing the restoration, economic considerations, expectations patients have for their investment, cost-effectiveness, influences of the health care system on how and for whom restorations are to be placed, and global challenges that limit the types of materials available in different areas of the world. The quandary is to find ways to actively engage multiple stakeholders to agree on priorities and future actions to focus future directions on the creation of more ideal restorative materials that can be available throughout the world.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".