Bibliometric Analysis of Oral Mucositis Research In Pediatric Oncology
Bibliographic record
Abstract
Abstract Background: Individuals receiving cancer treatment experience many treatment-related complications. Since one of these complications is oral mucositis, studies are conducted on this subject. This study aims to analyse the studies examining oral mucositis in the pediatric oncology population by bibliometric methods.Methods: Bibliometric analysis was performed using the WosViewer software to scan the articles written in the relevant field. Publication trend, country distribution, journal and citation analysis, citation analysis of publications, keyword analysis of publications, text mining of abstracts were performed. Results: In this study, 108 studies in the Web of Science database were examined. Because of the analyses, it was determined that there was an increase in studies on the subject after 2017. It has been determined that America, Brazil and Canada are the countries with the highest number of studies on this subject. Oral mucositis, mucositis and chemotherapy were determined as the most frequently used keywords.Discussion: Studies of oral mucositis in the pediatric population tend to increase recently. Preserving this increase and accelerating the work to be done in this field will fill the gaps in the literature.
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 | 0.013 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.158 | 0.180 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".