Scientific Interests of 21st Century Clinical Oral Implant Research: Topical Trend Analysis
Bibliographic record
Abstract
BACKGROUND: Discrimination between ongoing and solved research questions may help to distinguish established dogmas from evidence-based implant dentistry. PURPOSE: The purpose of this study was to investigate topics of interest in the field of oral implant science and evolving thematic trends in clinical studies during the last decade. MATERIALS AND METHODS: Electronic and manual searches of English literature were performed to identify clinical studies on oral implants. Out of 15,695 publications screened, 2,875 clinical investigations were included. RESULTS: Among the most prevalent topics were immediate loading (14.3%), bone substitutes (11.6%), lateral sinus grafting (10.7%), implant overdentures (10.5%), single-tooth implant crowns (8.8%), cross-arch implant bridges (8.0%), immediate implant placement (7.5%), implant surfaces (7.0%), simultaneous implant placement and augmentation (6.4%) as well as guided bone regeneration (5.3%). Significant increase of scientific interest was seen in immediate loading (+6.3%, p < .001), platform switching (+2.9%, p < 0.001), lateral sinus grafting (+2.3%, p = .024), flapless implant surgery (+2.2%, p < 0.001), and guided implant surgery (+1.9%, p = .011), while research on implant overdentures (-6.6%, p = .033) and tooth-to-implant connection (-2.5%, p = .010) was on the decline. CONCLUSIONS: Literature coverage, since the beginning of the 21st century, has seen greater focus on surgical topics compared to prosthodontic issues (p = .005) while only few topics experienced decrease of interest indicating scientific consensus.
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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 | 0.021 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.038 | 0.047 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".