Development of prognostic model for fistula maturation in patients with advanced renal failure
Bibliographic record
Abstract
This study aimed to explore the role of patient’s characteristic and haematological factors as predictive on the maturation of arteriovenous fistulae in patients who underwent vascular access surgery at the Royal Infirmary of Edinburgh. Retrospective data from 300 patients who had undergone fistula creation between February 2007 and October 2010 was examined. A predictive logistic regression model was developed using the backward stepwise procedure. Model performance, discrimination and calibration, was assessed using the receiver operating characteristics (ROC) curve and Hosmer and Lemeshow goodness of fit test. Three variables were identified which independently influenced fistula maturation. Males were twice as likely to undergo fistula maturation, compared to that of females (odds ratio (OR) 0.514; 95% confidence interval (CI) 0.308–0.857), patients with no evidence of peripheral vascular disease (PVD) were three times more likely to mature their fistula (OR 3.140; 95% CI 1.596–6.177) and a pre-operative vein diameter > 2.5 mm resulted in a fivefold increase in fistula maturation compared to a vein size less than 2.5 mm (OR 4.532; 95% CI 2.063–9.958). The model for fistula maturation had fair discrimination as indicated by the area under the ROC curve (0.68; 95% CI 0.615–0.738) but good calibration indicated by Hosmer and Lemeshow test ( p = 0.79). Gender, PVD and vein size are independent predictors of arteriovenous fistula maturation. The clinical utility of these risk equation in the maturation of arteriovenous fistulae requires further validation in the newly treated patients.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".