Publication trends in pediatric renal transplantation: Bibliometric analysis of literature from 1950 to 2017
Bibliographic record
Abstract
INTRODUCTION: Pediatric renal transplantation has been heavily published since the 1950s. Herein, we describe the bibliometrics and impact of the 200 most-cited pediatric renal transplantation manuscripts. METHODS: We identified pediatric renal transplantation publications from 1900 onwards. Year, citations, h-index, geographic origin, impact factor, topic, and design of the 200 top-cited papers were extracted. Impact index was calculated, adjusting for citation volume and time since publication. RESULTS: Of the top 200 papers, mean citation count was 80 ± 40, impact factor 3.9 ± 3.7, h-index 35 ± 20, and impact index 25 ± 13. Studies were mostly retrospective (31%) or observational (32%). Most papers originated from the United States (58%), Germany (9%), and Italy (6%), which did not correlate with citation counts. Transplantation (18%), Pediatric Nephrology (16%), and American Journal of Transplantation (11%) had the highest publication volume, which did not correlate with citation count. The main topics were medical renal disease, drug monitoring, compliance, and viruses. Most of the top-cited papers (179; 90%) were published after 1991. The difference in the number of times cited between papers published before and after 1991 was insignificant (75 ± 24 vs 80 ± 42; P = 0.59). There was a difference in impact index for the same period (48 ± 15 vs 22 ± 10; P < 0.01). CONCLUSIONS: The most-cited papers were concentrated in three journals, but the top three cited papers were published elsewhere. Recent publications were more cited with a higher impact than older papers. Despite the importance of surgery in transplantation, there is a paucity of high-impact papers on this topic.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.796 | 0.927 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".