Bibliometric analysis of cerebral organoids and diseases in the last 10 years
Bibliographic record
Abstract
Cerebral organoids have emerged as a powerful tool for mirroring the brain developmental processes and replicating its unique physiology. This bibliometric analysis aims to delineate the burgeoning trends in the application of cerebral organoids in disease research and offer insights for future investigations. We screened all relevant literature from the Web of Science on cerebral organoids in disease research during the period 2013-2022 and analyzed the research trends in the field using VOSviewer, CiteSpace, and Scimago Graphica software. According to the search strategy, 592 articles were screened out. The United States of America (USA) was the most productive, followed by China and Germany. The top nine institutions in terms of the number of publications include Canada and the United States, with the University of California, San Diego (USA), having the highest number of publications. The International Journal of Molecular Sciences was the most productive journal. Knoblich, Juergen A., and Lancaster, Madeline A. published the highest number of articles. Keyword cluster analysis showed that current research trends focused more on induced pluripotent stem cells to construct organoid models of cerebral diseases and the exploration of their mechanisms and therapeutic modalities. This study provides a comprehensive summary and analysis of global research trends in the field of cerebral organoids in diseases. In the past decade, the number of high-quality papers in this field has increased significantly, and cerebral organoids provide hope for simulating nervous system diseases (such as Alzheimer's disease).
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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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.193 | 0.264 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".