Global Dialysis Vascular Access Care: A Multi-Specialty Interest
Bibliographic record
Abstract
Background: The history of dialysis vascular access dates back to 1924 in Germany when Dr. George Haas connected an artery and vein using a glass cannula. Since then, dialysis vascular access care has evolved robustly through scientific contributions from researchers worldwide. We sought to identify the global distribution and contribution of medical specialties to the medical literature on dialysis vascular access care over the past 3 decades. Methods: We performed a thorough literature search of articles related to dialysis vascular access published in the English medical literature from 1991 to 2021. We identified and analyzed 2,768 articles from 74 countries worldwide and stratified them by article type and medical specialty. Results: Out of 2,768 articles, 41.5% (1148) originated from the United States, followed by China (5.1%), United Kingdom (4.6%), Germany (3.6%), India (3.4%), Japan (3.1%) and Canada (2.9%). Forty-three percent of search results (1205) were observational studies, followed by 27% (761) case reports/series, 16.5% (458) review articles, 12% (335) clinical trials and 0.3% (9) meta-analyses. The majority of articles (49%) were published in nephrology journals, followed by 14%, 10%, 8%, and 4% of articles published in general medicine, surgery, vascular medicine, and interventional radiology journals, respectively. Conclusions: Dialysis vascular access care is provided by specialists with multiple backgrounds across the globe. Thirty-nine percent of the evidence is published from developing countries. Retrospective observational studies along with case reports/series provide 88% of the current evidence for clinical practice. Barely 12% of the published literature is from prospective clinical trials. Even though providers with multiple training backgrounds are involved with dialysis vascular access care, almost 49% of the scientific evidence is published in journals catering to the nephrologists. The literature trend highlights the need for better collaboration across all specialties to effectively improve patient care.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".