Gender disparity in fistula use at initiation of hemodialysis varies markedly across ESRD networks—Analysis of USRDS data
Bibliographic record
Abstract
BACKGROUND: Gender disparities had been noted in the care of women with end stage renal disease (ESRD) in the early 2000's, including less frequent initiation of hemodialysis utilizing a fistula but more recent data have not been examined and underlying factors have not been extensively studied. STUDY DESIGN: Data from the United States Renal Data System (USRDS) were examined, including 202,999 hemodialysis patients. Only those who had received prior nephrology care were included. Multiple logistic regression was used, adjusted for possible confounders, including age, race, cause of ESRD, BMI, height, history of alcohol or drug abuse, medical comorbidities, ability to ambulate, time of nephrology care, type of insurance, and ESRD network. RESULTS: The odds of arteriovenous fistula (AVF) use at initiation of hemodialysis were significantly lower in women compared to men (OR = 0.69, 95% CI 0.67-0.71, P < 0.0001). The gender gap in AVF use at initiation was highest in New York and the upper Midwest (networks 2 and12) and smallest for Southern California and the Pacific Northwest and Alaska (18 and 16). Gender disparity was more pronounced for black women, with odds ratios for AVF use at initiation of dialysis (OR = 0.66, 95% CI 0.62-0.69), P < 0.0001 as compared to non-black (OR 0.70, 95% CI 0.68-0.73), P ≤ 0.0001. LIMITATIONS: Limitations include use of USRDS data. Data misclassification or errors in data reporting may exist and certain comorbid conditions may be underreported. Data regarding rate of primary fistula non-function are also not available. CONCLUSION: Adjusted odds ratio for AVF use was significantly lower in women compared to men, independent of time of nephrology care and other predictors. The gender disparity was most pronounced for black women and also varied from 20% to 46% lower odds for AVF use in women for different ESRD networks, after controlling for possible confounding variables, suggesting that practice based factors may be of importance in explaining this important finding.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".