Interventional nephrology: from episodic to coordinated vascular access care
Bibliographic record
Abstract
In recent years, nephrologists have taken the initiative of performing vascular access-related procedures themselves. Because of their unique clinical perspective on dialysis access and better understanding of the intricacies of renal replacement therapy, nephrologists are ideally suited for this activity. This approach has minimized delays, decreased hospitalizations and decreased the use of temporary catheters, thereby improving medical care, decreasing costs and increasing patient convenience. Vascular access interventions commonly employed by nephrologists include vascular access education, vascular mapping, percutaneous balloon angioplasty, thrombectomy, intravascular coil and stent insertion and tunneled hemodialysis catheter-related procedures. While the performance of these procedures by nephrologists offers many advantages, appropriate training to develop the necessary procedural skills is critical. Recent data have emphasized that a nephrologist can be successfully trained to become a competent interventionalist. In addition to documenting excellent outcome data, multiple reports have demonstrated the safety and success of an interventional nephrology approach. The last decade has been a period of significant advances in this new field. This has been driven in part by the formation of the American Society of Diagnostic and Interventional Nephrology (ASDIN), whose mission includes training, quality assurance and certification. Recently, the ASDIN has published guidelines for training in nephrology-related procedures and has begun certifying physicians in specific procedures related to chronic kidney disease. It is anticipated that this will promote the skillful performance of these procedures by nephrologists and lead to substantial improvements in the care of renal patients. Challenges for the future include awareness of this subspecialty and development of training programs at academic centers on a larger scale.
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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 | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".