Analysis of the top 100 most influential papers in benign prostatic hyperplasia
Bibliographic record
Abstract
INTRODUCTION: The fund of knowledge on benign prostatic hypertrophy (BPH) has been growing since the 1970s. Citation analysis is a tool by which we can quantify influence of specific articles and assess the growth of a certain topic. This paper seeks to identify trends, as well as draw attention to the most influential papers, authors, and journals. Many analogous studies have been done, but none have been done in the field of BPH. METHODS: We used Thomson Reuters Web of Science to collect articles pertaining to BPH in a two-step fashion. We identified 117 keywords relevant to BPH and using these 117 words, we were able to identify 7302 total articles. These articles were organized by number of citations. Of the top 200 articles, 100 articles were excluded based on title and abstract analysis. One hundred articles were included for final analysis, as this is the standard of citation analysis. RESULTS: Overall, total citations were slightly correlated with journal impact factor. Author analysis revealed no significant difference between authorship and average citations. Topic analysis showed the most cited topic was surgical management with 657.35 citations per year. Study design analysis showed the predominant study design was the randomized control trial. CONCLUSIONS: By using the two-step methodology, we were able to create a list of the top 100 most influential articles in the field of BPH. In doing so, we illustrated the growth of the field over time and paid tribute to the myriad of papers, authors, and journals that have shaped the field to this day.
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 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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.088 | 0.073 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".