Knowledge-map analysis of percutaneous nephrolithotomy (PNL) for urolithiasis
Bibliographic record
Abstract
Percutaneous nephrolithotomy (PNL) has been used in the treatment of urolithiasis for more than 20 years. However, bibliometric analysis of the global use of PNL for urolithiasis is rare. We retrieved the literatures on PNL and urolithiasis from Web of science core collection database. VOSviewer was used to analyze keywords, citations, publications, co-authorship, themes, and trend topics. A total of 3103 articles were analyzed, most of which were original ones. The most common keywords were "percutaneous nephrology" and "urolithiasis", both of which were closely related to "ureteroscopy". Journal of Urology and Zeng Guohua from the First Affiliated Hospital of Guangzhou Medical University were the most published journal and author in this field. The most productive country was the United States, and its closest partners were Canada, China, and Italy. The five hot topics were the specific application methods and means, risk factors of urolithiasis, the development of treatment technology of urolithiasis, the characteristics, composition, and properties of stones, and the evaluation of curative effect. This study aimed to provide a new perspective for PNL treatment of urolithiasis and provided valuable information for urologic researchers to understand their research hotspots, cooperative institutions, and research frontiers.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".