Bibliometric profile of neurogenic bladder in the literature: a 20-year bibliometric analysis
Bibliographic record
Abstract
Neurogenic bladder is a dysfunction of the lower urinary tract caused by nervous system disorder. We investigated the trends in publication of articles under the topic "neurogenic bladder" using bibliometric analysis. Articles on neurogenic bladder, published between 1995 and 2014, were retrieved from the ISI Web of Science citation database. We analyzed the search results for authors, countries, institutions, journals, and top-cited papers. A total of 1,904 articles were retrieved. There was a small increase in the number of articles on neurogenic bladder from 1995 (n = 43) to 2014 (n = 117). The USA was the leading country in the total number of articles (n = 598). However, the number of publications from China has rapidly increased, and China was ranked second in 2014. Emmanuel Chartier-Kastler (n = 65) was the most productive author, and University of Paris VI (Paris 6) (n = 61) was the most productive institution. The Journal of Urology published the greatest number of articles on this topic (n = 285). Articles on neurogenic bladder were often published in a professional journal under the category Urology & Nephrology, Neurosciences & Neurology, or Rehabilitation. Visualization analysis based on co-citation networks was conducted using CiteSpace III. Visualization analysis revealed that the hot spots in neurogenic bladder were botulinum toxin-A, prazosin, bethanechol, and afferent pathways. These findings provide new insight into the publication trends and hot spots in neurogenic bladder.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.157 | 0.182 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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, 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".