Steal syndrome complicating upper extremity hemoaccess procedures: incidence and risk factors.
Bibliographic record
Abstract
INTRODUCTION: Steal syndrome is a potentially grave complication of upper extremity hemoaccess (HA) in patients with renal failure. To determine the incidence and risk factors for steal in these patients at the St. Boniface Hospital, Winnipeg, a tertiary care centre for vascular surgery and dialysis, we reviewed data from patients requiring hemodialysis between September 1986 and July 2000. PATIENTS AND METHODS: We excluded all venous catheter and lower extremity procedures. There remained 325 upper extremity procedures in 217 patients. Data were collected from the patients' charts or by interview. First by univariate analysis and then by multivariate analysis for independent risk factors, we studied the effect on the development of steal of age, sex, race diabetes mellitus, hypertension, coronary artery disease or cerebrovascular disease, smoking, proximal procedures based on the brachial artery, distal procedures based on the radial artery, the use of prosthetic graft material and the creation of autologous fistulas. RESULTS: The incidence of steal was 6.2%. The significant independent risk factors were diabetes mellitus (odds ratio [OR] 5.00, 95% confidence interval [CI] 1.39-18.08, p = 0.01) and Aboriginal race (OR 3.59, 95% CI 1.07-12.04, p = 0.04). An increasing risk for each year of advancing age at the time of procedure was suggested but was not significant (OR 1.04, 95% CI 1.00-1.09 p = 0.07). CONCLUSIONS: Patients who are diabetic or Aboriginal are at increased risk for steal with upper extremity HA procedures. This knowledge can guide discussion of dialysis options and informed consent. If upper extremity HA procedures are undertaken in patients at risk, they should be closely monitored and early intervention applied if necessary.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".