DIALYSIS VASCULAR ACCESS
Bibliographic record
Abstract
Introduction and Aims: On-line dialysance (Kt) and thermodilution (BTM-Qa) methods could be important components in vascular access monitoring programs.This study evaluated the efficiency of these two methods in reducing the thrombosis rate and access-related costs compared with a historic control group.Methods: We studied 148 long-term hemodialysis patients with arteriovenous fistulas (historical control group, n = 74) for 2 years.During the study period, the indications for vascular treatments were the Kt reduction ≥20% with respect to baseline values or Qa less than 500 mL/min (or a decrease in flow > 20%).Differences between the Qa and Kt groups were tested using Student's T-Test or the Wilcoxon test, as appropriate.The χ2 test was used to analyze the angioplasty and thrombosis rates compared with the historical control group.A P-value ≤0.05 was considered statistically significant.Results: During the study period, we detected 16 cases of significant vascular access dysfunction.The Kt value after vascular treatment was 71.1L (59L; P = 0.001) and BTM-Qa was 1218.6 mL/min (519.7 mL/min; P = 0.001).Compared with the control group, the thrombosis rate was 0.027 vs 0.148 episodes/patient-year (P = 0.009) and the total access-related cost was €22,293 vs €47,467 (P = 0.033).Conclusions: This study suggests that a combined monitoring program based on Kt and Qa-BTM represents an effective screening method that significantly reduces the thrombosis rate and economic costs of vascular treatments SP523
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.018 |
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".