Comparison of standard forearm prosthetic loop grafts to composite semiloop forearm grafts (“semi‐grafts”) in hemodialysis patients: A prospective study
Bibliographic record
Abstract
INTRODUCTION: To prospectively assess the performance of composite semiloop antebrachial grafts ("semi-grafts," SGs) in hemodialysis patients. METHODS: Eighty-five patients who received 67 loop antebrachial grafts (LG-group) and 25 antebrachial semigrafts (SG-group) were enrolled. SGs were defined as those originating from the brachial artery and anastomosed with the proximal mature mid-antebrachial cephalic vein. Cephalic vein length should be at least 10 cm in length and of ≥5 mm in diameter for inclusion in the SG-group. LG-group included all possible outflow vein options of minimum diameter 3 mm. Kaplan-Meier statistics was used for comparison of groups. FINDINGS: Main indication for a SG was a failing radiocephalic fistula with extensive distal cephalic vein stenosis not amenable to correction or failed after endovascular repair or requiring long interposition grafting. The mean follow-up period was 20.16 ± 22.6 and 29.6 ± 36.7 months for the LG- and SG-group, respectively (P = 0.14). Forty-two patients died during the follow-up. Primary patency (up to first intervention or failure) at 6 and 12 months for LG- vs. SG-group was 93.9% vs. 83.7% and 47% vs. 55.8% (P = 0.08). Secondary patency (up to abandonment) was 58.2% vs. 61.1% and 36% vs. 45.8% at 12 and 24 months (P = 0.18). Mortality at 48 months was 22.4% (LG-group) and 24% (SG-group) (P = 0.9). DISCUSSION: There was a trend toward better primary and secondary patency rates for the SGs especially in the long-term. This is a valuable option in selected patients that access surgeons and nephrologists should be aware of.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".