Predictive parameters of arteriovenous fistula maturation in patients with end-stage renal disease
Bibliographic record
Abstract
BACKGROUND: The objevctive of the present study was to explore the potential influence of blood markers and patient factors such as risk factors, kidney function profile, coagulation profile, lipid profile, body mass index, blood pressure, and vein diameter on the maturation of arteriovenous fistula (AVF) in patients with end-stage renal disease. METHODS: Retrospective data from 300 patients who had undergone AVF creation at the Royal Infirmary of Edinburgh were examined. A predictive logistic regression model was developed using a backward stepwise procedure. Model performance, discrimination, and calibration were assessed using the receiver operating characteristic (ROC) curve and Hosmer-Lemeshow goodness-of-fit test. The final model was externally validated by 100 prospective patients who received a new fistula at the Royal Infirmary of Edinburgh. RESULTS: > 0.05). CONCLUSION: Gender, PVD, and vein size are independent predictors of AVF maturation. The clinical utility of these risk categories in the maturation of AVF requires further evaluation in longer follow-up.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".