Bibliographic record
Abstract
In 1978, Bill Bennett hired me to “do academic dialysis.” As part of the first National Kidney Foundation Dialysis Outcome Quality Initiative Clinical Practice Guideline leadership team, I became more interested in hemodialysis vascular access. I designed and placed “save the vessels” bracelets for our patients to wear protecting vessels. When I considered hemodialysis to be likely within 12 months, I strongly encouraged creation of arteriovenous fistulae (AVFs). I delayed arteriovenous graft (AVG) placement until much closer to the expected hemodialysis initiation date. Over 35 years I noticed that a significant fraction of patients with functioning AVFs seemed to slow the decline in their rate of eGFR. In a simple proof-of-concept retrospective observational study, it appeared that eGFR decline did display a slowing after AVF creation (1). Subsequent better-designed studies corroborated our findings (2⇓⇓–5). Although fraught with methodological problems, one study suggested that even placing a peritoneal dialysis catheter slowed progression (4). This did not make physiologic sense to me. In this issue of Kidney360 , another study from Montreal dispels the notion that placing of a peritoneal dialysis catheter affects eGFR decline (5). Sumida et al. utilized a Veterans Administration database, which included AVGs and AVFs with over 3000 patients, not limited by AVF maturation, and used central venous catheter recipients as propensity-matched controls (2). AVF and AVG recipients showed eGFR deceleration after surgical creation, independent of maturation, whereas our study only evaluated maturing AVFs. The first Montreal paper precisely defined the cohort, its clinical characteristics, tried to adjust for known confounders, and used maturation for inclusion (3). Attenuation of eGFR decline over time was again noted after AVF creation. At about the same time, Lundstrom et al. (4) were utilizing a Swedish national database and used patients with peritoneal dialysis catheters as comparators …
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".