Utility of ultrasonographic venous assessment prior to forearm arteriovenous fistula creation
Bibliographic record
Abstract
AIM: The purpose of this study was to evaluate the clinical utility of Doppler ultrasound (US) prior to native forearm arteriovenous fistula (AVF) creation. MATERIALS AND METHODS: US mapping was carried out pre-operatively to evaluate the major veins and arteries in the appropriate arm. One hundred and 6 patients were identified retrospectively over 2 years with complete clinical and US data. A failed fistula was defined as an inability to provide blood flow to meet adequacy targets by 6 months (urea reduction ratio > or = 65%). RESULTS: Twenty-nine patients (27.4%) had successful forearm AVFs. The mean minimum forearm cephalic vein diameter (CVD) was 2.51 +/- 0.14 and 2.23 +/- 0.06 mm in successful and failed fistulae, respectively (p = 0.04). This result was primarily due to differences observed in women. A receiver operator curve analysis showed that a cutpoint of 2.6 mm for minimum forearm CVD had the greatest predictive value with a likelihood ratio of 3.94 (95% CI: 1.97 - 7.84) for fistula failure. Multivariate logistic regression analysis determined that male gender and minimum forearm CVD were the only significant predictors for fistula success with odds ratios of 3.90 (95% CI: 1.30 - 11.68) and 2.31 (95% CI: 1.00 - 5.43), respectively. The study is limited by the possibility that US results in patients may have lead to an alternative type of access being attempted. CONCLUSIONS: US mapping prior to forearm AVF creation is of modest benefit. Only male gender and minimum forearm CVD were predictive of AVF success.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".