Global Trends in Neurosurgical Nursing Research from 2014 to 2024: A Bibliometric and Visualization Study
Bibliographic record
Abstract
As neurosurgical nursing continues to evolve alongside technological advancements, a systematic examination of its research landscape has become increasingly imperative. This study employed bibliometric analysis to investigate global research trends in neurosurgical nursing from 2014 to 2024, utilizing 5677 records extracted from the Web of Science Core Collection (WOSCC). Through the application of bibliometric techniques and CiteSpace visualization tools, we quantitatively analyzed publication outputs, geographical distributions, institutional contributions, and keyword evolution patterns. Our temporal analysis revealed three key findings: (1) a steady annual growth in publication volume (average increase of 12.3% per year), with the United States contributing 39.90% of total publications; (2) dominant institutional contributors including Harvard University, the University of California System, and the University of Toronto, which collectively accounted for 28.5% of high-citation publications; and (3) emerging research foci centered on seven primary themes: clinical interventions ("clinical article", "external ventricular drain"), patient populations ("neurosurgical patients"), treatment modalities ("radiotherapy"), evidence synthesis ("meta analysis"), care delivery models ("patterns"), and anatomical considerations ("central nervous system"). These findings provided empirical evidence for understanding current research priorities, identifying knowledge gaps, and forecasting future developmental trajectories in neurosurgical nursing. The study established a foundational bibliometric framework that may guide strategic research planning and international collaboration in this specialized nursing field.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.109 | 0.195 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".