Geographic disparities in arteriovenous fistula placement in patients approaching hemodialysis in the <scp>U</scp>nited <scp>S</scp>tates
Bibliographic record
Abstract
Arteriovenous fistula (AVF) is the preferred vascular access for hemodialysis (HD). Several factors associated with AVF placement have been identified (e.g., age, sex, race, comorbidities). We hypothesized that geographic location of patient residence might be associated with the probability of AVF placement as the initial access. We used the data from the United States Renal Data System (USRDS) database (2005-2008) linked to Medicare claims (2003-2008). Logistic regression was used to estimate specific characteristics of population associated with the AVF as first access placed or attempted for HD initiation. Our primary variable of interest was the geographic location, and the multivariate model was adjusted for age, sex, race, body mass index, primary cause of end-stage renal disease (ESRD), duration of pre-ESRD nephrology care, comorbidities, employment status, substance abuse, and income. Geographic location was determined using the data collected by the RUCA project and divided population into metropolitan, micropolitan, and rural categories. Patients (n = 111,953) identified from the USRDS database with linked Medicare claims were examined. Rates of fistula placement in the metropolitan, micropolitan, and rural population were 18.5%, 22.4%, and 21.6%, respectively. In comparison, patients who received catheter as the first access were 81.5%, 77.6% and 78.4%, respectively. The odds ratio of AVF placement as a first HD access in the rural and metropolitan population compared with the micropolitan population were 0.96 (0.90-1.03; P = 0.26) and 0.80 (0.76-0.84; P < 0.001), respectively. Our results indicate the presence of geographic disparities in AVF placement with decreased rates of AVF as the first access created in the metropolitan (but not rural) populations compared with the micropolitan communities.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".