Bibliometric analysis of the scientific production of literature on peri‐implant diseases in the Web of Science
Bibliographic record
Abstract
BACKGROUND: The exponential increase in implant placement worldwide and the high prevalence of its associated pathologies have prompted an increasing contribution by the scientific community to the number of publications related to peri-implant pathologies. PURPOSE: The objective of this work is to carry out a bibliometric analysis of scientific production on peri-implant diseases. MATERIALS AND METHODS: The search strategy included titles, keywords, and abstracts based on the term peri-implantitis and all the possible combinations existing in Science Citation Index Expanded (SCIE) of the main collection of Web of Science. Two limits were established: the document typology was limited to Article and Review, and articles published up to 2019 were selected. All articles were refined and standardized manually to avoid typographical errors and duplications in authors' names or institutions. RESULTS: The total number of papers collected was 2547. A significant increase was observed in the number of articles published, especially in the past 10 years. The three most productive authors were Europeans, and the 45 most productive institutions were the universities. The most productive funding entities were the governments. Of the published works, 42.28% were funded. Of the 2547 records, 86.53% presented keywords. CONCLUSIONS: Scientific literature on peri-implantitis shows scientific growth in recent years, with a growing trend towards collaboration between authors and institutions. Most of the works have been published in high-impact journals, and in the last 2 years, more than half of the works have received some type of public or private funding.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.015 | 0.151 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".