A Systematic Bibliometric Analysis of High-Impact Articles in Critical Care Nephrology
Bibliographic record
Abstract
INTRODUCTION: Critical care nephrology is a subspecialty that merges critical care and nephrology in response to shared pathobiology, clinical care, and technological innovations. To date, there has been no description of the highest impact articles. Accordingly, we systematically identified high impact articles in critical care nephrology. METHODS: This was a bibliometric analysis. The search was developed by a research librarian. Web of Science was searched for articles published between January 1, 2000 and December 31, 2020. Articles required a minimum of 30 citations, publication in English language, and reporting of primary (or secondary) original data. Articles were screened by two reviewers for eligibility and further adjudicated by three experts. The "Top 100" articles were hierarchically ranked by adjudication, citations in the 2 years following publication and journal impact factor (IF). For each article, we extracted detailed bibliometric data. Risk of bias was assessed for randomized trials by the Cochrane Risk of Bias tool. Analyses were descriptive. RESULTS: The search yielded 2,805 articles. Following initial screening, 307 articles were selected for full review and adjudication. The Top 100 articles were published across 20 journals (median [IQR] IF 10.6 [8.9-56.3]), 38% were published in the 5 years ending in 2020 and 62% were open access. The agreement between adjudicators was excellent (intraclass correlation, 0.96; 95% CI, 0.84-0.99). Of the Top 100, 44% were randomized trials, 35% were observational, 14% were systematic reviews, 6% were nonrandomized interventional studies and one article was a consensus document. The risk of bias among randomized trials was low. Common subgroup themes were RRT (42%), AKI (30%), fluids/resuscitation (14%), pediatrics (10%), interventions (8%), and perioperative care (6%). The citations for the Top 100 articles were 175 (95-393) and 9 were cited >1,000 times. CONCLUSION: Critical care nephrology has matured as an important subspecialty of critical care and nephrology. These high impact papers have focused largely on original studies, mostly clinical trials, within a few core themes. This list can be leveraged for curricula development, to stimulate research, and for quality assurance.
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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 | 0.077 | 0.319 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.012 |
| Bibliometrics | 0.300 | 0.261 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".