Bibliographic record
Abstract
Effective hemodialysis requires a reliable vascular access. Clinical practice guidelines strongly recommend the fistula as the preferred option followed by arteriovenous (AV) grafts, with central venous catheters being least preferred. Recently, there has been a growing awareness of the limitations of the fistula, its high rate of primary failure and that a fistula may not be appropriate for all patients initiating or on hemodialysis. However, determinates for fistula eligibility have not been clearly defined. The creation and use of a fistula requires the complex integration of patient, biological, and surgical factors, none of which can be easily predicted or planned. There have been several successful initiatives over the last decade addressing patient suitability for AV access, but none have validated defined criteria for fistula eligibility. We discuss these initiatives by addressing: 1) process of care, 2) radiological and nonradiological tests and procedures, and 3) alternative surgical approaches. Careful clinical judgment, appropriate vascular access assessment and placement, and an individualized approach to the risks and benefits will optimize patient health outcomes while minimizing prolonged catheter dependence among hemodialysis patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.076 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".