Vascular imaging for hemodialysis vascular access planning
Bibliographic record
Abstract
INTRODUCTION: Central venous catheters (CVC) increase risks associated with hemodialysis (HD), but may be necessary until an arteriovenous fistula (AVF) or graft (AVG) is achieved. The impact of vascular imaging on achievement of working AVF and AVG has not been firmly established. METHODS: Retrospective cohort of patients initiating HD with CVC in 2010-2011, classified by exposure to venography or Doppler vein mapping, and followed through December 31, 2012. Standard and time-dependent Cox models were used to determine hazard ratios (HRs) of death, working AVF, and any AVF or AVG. Logistic regression was used to assess the association of preoperative imaging with successful AVF or AVG among 18,883 individuals who had surgery. Models were adjusted for clinical and demographic factors. FINDINGS: Among 33,918 patients followed for a median of 404 days, 39.1% had imaging and 55.7% had surgery. Working AVF or AVG were achieved in 40.6%; 46.2% died. Compared to nonimaged patients, imaged patients were more likely to achieve working AVF (HR = 1.45 [95% confidence interval [CI] 1.36, 1.55], P < 0.001]), any AVF or AVG (HR = 1.63 [1.58, 1.69], P > 0.001), and less likely to die (HR = 0.88 [0.83-0.94], P < 0.001). Among patients who had surgery, the odds ratio for any successful AVF or AVG was 1.09 (1.02-1.16, P = 0.008). DISCUSSION: Fewer than half of patients who initiated HD with a CVC had vascular imaging. Imaged patients were more likely to have vascular surgery and had increased achievement of working AV fistulas and grafts. Outcomes of surgery were similar in patients who did and did not have imaging.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".