The Research Trends of Post-operative Pediatric Cerebellar Mutism Syndrome: A Bibliometric Analysis (1999-2022)
Bibliographic record
Abstract
Abstract Background Post-operative pediatric cerebellar mutism syndrome (ppCMS) is a common neurological complication characterized by delayed onset mutism, emotional lability, hypotonia, and oropharyngeal dysfunction following resection of a posterior fossa tumor in children. The objective of this study is to visually depict the knowledge structure and pinpoint research hotspots within the field using bibliometric analysis. Method Publications related to ppCMS from 1999 to 2022 were searched on the Web of Science Core Collection (WoSCC) database. VOSviewer, R package, “bibliometrix”, and CiteSpace were used to draw and analyze corresponding visualization maps. Results 410 articles from 52 countries led by the United States of America (USA) and England were included. The number of published papers is on the rise in general. Hospital for Sick Children (Canada), St. Jude Children’s Research Hospital (USA), University Toronto (Canada), Texas Children’s Hospital (USA), and Children’s National Hospital (USA) are the main research institutions. Child’s Nervous System is the most popular and the most co-cited journal in this domain. These publications come from 2091 authors. Gajjar, A. has published the most papers, and the papers authored by Schmahmann, J.D. have been co-cited the most. The mechanisms, risk factors, and clinical manifestations of ppCMS occurrence and development are the main topics in this field. The most commonly used keywords are medulloblastoma, posterior fossa syndrome, cerebellar mutism, cerebellum, and children. Conclusion This is the first bibliometric analysis to comprehensively overview the active research areas and development of ppCMS, which will provide a reference for scholars studying this field.
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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 | Not applicable | high |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | 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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.145 | 0.183 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".