Status of Research on Dental Caries during Pregnancy: A Biometric Exploration
Bibliographic record
Abstract
AIM: To describe the bibliometric characteristics of the state of scientific production on dental caries during pregnancy. MATERIALS AND METHODS: A bibliometric study with a sample of 408 publications indexed in the Scopus database, which were selected based on a search strategy that included logical operators and the term MESH. The Scival tool was used to analyze the metadata. RESULTS: Jamieson Lisa Marie is positioned as the author with the most publications, and Schroth Robert as the one with the best-weighted impact (FWCI: 37.7). High-impact journals such as BMC Oral Health, Journal of Dental Research, and BMC Public Health stand out for their productivity and an average number of citations. The United States has a large number of publications and evidence of networks of scientific activity with Australia, Canada, and Brazil. The University of Adelaide leads the production and the work of two Latin American institutions (Universidade de São Paulo and Universidade Federal do Maranhão) stands out. International collaboration has improved during the study period. CONCLUSION: Scientific production on dental caries during pregnancy is increasingly published in high-impact journals, with growing international collaboration. The United States leads in publications, while Australian institutions are the most productive in this field. CLINICAL SIGNIFICANCE: Caries during pregnancy is a problem that has an important impact on maternal and perinatal health; thus, its timely management and prevention are important areas of research in the field of oral health. How to cite this article: Muñoz-Hidalgo M, Verastegui-Cabanillas Y, Barja-Ore J. Status of Research on Dental Caries during Pregnancy: A Biometric Exploration. J Contemp Dent Pract 2024;25(4):386-391.
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 | 0.012 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.069 | 0.103 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".