Current applications and research trends of ultrasound examination in acute kidney injury assessment: a bibliometric analysis
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) is a significant clinical condition, and ultrasound examination has emerged as a crucial non-invasive imaging method for assessing kidney status, especially in its diagnosis and management. This study aims to perform a bibliometric analysis to clarify current research trends in ultrasound assessment of AKI. METHODS: We conducted a literature search in the Web of Science database using keywords related to ultrasound examinations of acute kidney injury, up to November 15, 2023. The results were analyzed using the bibliometric software package in R. Relevant literature information was analyzed. RESULTS: A total of 1109 articles were included in the study. Research papers published between 2019 and 2024 demonstrated a significant upward trend. The United States, China, and Italy ranked as the top three countries in terms of publication volume. Among the top 10 research institutions with the highest number of publications, 6 are in the United States, with Université de Montréal being the institution with the most publications. Keyword trends focused on: resistive index, risk factors, therapy, glomerular filtration rate, survival, etc. CONCLUSION: This bibliometric study highlights the advancements in ultrasound examination for AKI and underscores the importance of such analyses in determining research trends. Future research should emphasize the integration of various imaging techniques to improve diagnostic accuracy and clinical management of AKI.
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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 | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.020 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.214 | 0.244 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| 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, 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".