Bibliometric overview of the Top 100 most cited articles on hydrocephalus
Bibliographic record
Abstract
Background: Hydrocephalus is one of the most common brain disorders and numerous articles were published to address the clinical aspect and its management. This study aims to highlight the most influential work on hydrocephalus on bibliometric basis. Methods: author affiliation, country of origin, year and journal of publication, article's category, and citation count according to Scopus and Google scholar databases. Results: The 100 most cited articles were thoroughly analyzed. Publication dates ranged from 1946 to 2014, with most articles (45) published between 1998 and 2007. The mean number of citations per publication was 201 with total of 20,177 citations. The United States of America contributed half of the articles. The leading institution was the Canadian hospital for Sick Children University of Toronto having published 5 articles. Hydrocephalus in general and normal pressure hydrocephalus was the two major categories addressed with most studies fall under the topic of surgical management. Neurosurgery was the specialty with the greatest contribution (47%). The articles were published in 46 different journals led by the Journal of Neurosurgery with total of 17 articles. Conclusion: This bibliometric analysis delineates the landmark publications in hydrocephalus. The listed articles depict the myriad of studied aspects historically which helps in understanding hydrocephalus overall in evidence-based module for neurosurgeons and non-neurosurgeons.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.032 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".