Risk Equation Determining Unsuccessful Cannulation Events and Failure to Maturation in Arteriovenous Fistulas (REDUCE FTM I)
Bibliographic record
Abstract
Fistulas are the preferred permanent hemodialysis vascular access but a significant obstacle to increasing their prevalence is the fistula's high "failure to mature" (FTM) rate. This study aimed to (1) identify preoperative clinical characteristics that are predictive of fistula FTM and (2) use these predictive factors to develop and validate a scoring system to stratify the patient's risk for FTM. From a derivation set of 422 patients who had a first fistula created, a prediction rule was created using multivariate stepwise logistic regression. The model was internally validated using split-half cross-validation and bootstrapping techniques. A simple scoring system was derived and externally validated on 445 different, prospective patients who received a new fistula at five large North American dialysis centers. The clinical predictors that were associated with FTM were aged > or =65 yr (odds ratio [OR] 2.23; 95% confidence interval [CI] 1.25 to 3.96), peripheral vascular disease (OR 2.97; 95% CI 1.34 to 6.57), coronary artery disease (OR 2.83; 95% CI 1.60 to 5.00), and white race (OR 0.43; 95% CI 0.24 to 0.75). The resulting scoring system, which was externally validated in 445 patients, had four risk categories for fistula FTM: low (24%), moderate (34%), high (50%), and very high (69%; trend P < 0.0001). A preoperative, clinical prediction rule to determine fistulas that are likely to fail maturation was created and rigorously validated. It was found to be simple and easily reproducible and applied to predictive risk categories. These categories predicted risk of FTM to be 24, 34, 50, and 69% and are dependent on age, coronary artery disease, peripheral vascular disease, and race. The clinical utility of these risk categories in increasing rates of permanent accesses requires further clinical evaluation.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".