Dental implant quality registries and databases: A systematic review
Bibliographic record
Abstract
BACKGROUND: The importance of dental implant quality register has been well-documented. However, no systematic review conducted on dental implant quality register can be found in the literature. Therefore, the purpose of this study was to study the existed dental implant quality registries to explain the goals, data elements, and reports of dental implant quality registries. MATERIALS AND METHODS: This systematic study was conducted in PubMed, Scopus, Web of Science, and Embase databases. For evaluating dental implant quality registers, all studies in the English language were examined with no time limitation. Case reports, conference abstracts, and letters to the editor were excluded. The analysis of the quality of the studies was done by the STrengthening the Reporting of OBservational studies in Epidemiology checklist. RESULTS: The primary search identified 5565 articles. After eliminating duplicate articles and articles that did not meet the inclusion criteria and reviewing 40 full texts, 11 studies were included in this study. In this review, seven countries as Sweden, the USA, Canada, Germany, Finland, Australia, and South Korea had dental implant quality registers. Furthermore, the goals of dental implant quality registers were classified into the categories of research, epidemiology, administrative, clinical, and surveillance. CONCLUSION: The results of this study provide dentists and other stakeholders useful information on the existed dental implant quality registers and databases worldwide. It also provides a framework of the goals, data elements, and reports of dental implant quality registry. The establishment of dental implant quality register will be beneficial for societies and also allows them to control the complications of dental implants in future.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".