Variation in the Level of eGFR at Dialysis Initiation across Dialysis Facilities and Geographic Regions
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The relative influence of facilities and regions on the timing of dialysis initiation remains unknown. The purpose of the study is to determine the variation in eGFR at dialysis initiation across dialysis facilities and geographic regions in Canada after accounting for patient-level factors (case mix). DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: In total, 33,263 dialysis patients with an eGFR measure at dialysis initiation between January of 2001 and December of 2010 representing 63 dialysis facilities and 14 geographic regions were included in the study. Multilevel models and intraclass correlation coefficients were used to evaluate the variation in timing of dialysis initiation by eGFR at the patient, facility, and geographic levels. RESULTS: The proportion initiating dialysis with an eGFR≥10.5 ml/min per 1.73 m(2) was 35.3%, varying from 20.1% to 57.2% across geographic regions and from 10% to 67% across facilities. In an unadjusted, intercept-only linear model, 90.7%, 6.6%, and 2.7% of the explained variability were attributable to patient, facility, and geography, respectively. After adjustment for patient and facility factors, 96.9% of the explained variability was attributable to patient case mix, 3.1% was attributable to the facility, and 0.0% was attributable to the geographic region. These findings were consistent when the eGFR was categorized as a binary variable (≥10.5 ml/min per 1.73 m(2)) or in an analysis limited to patients with >3 months of predialysis care. CONCLUSIONS: Patient characteristics accounted for the majority of the explained variation regarding the eGFR at the initiation of dialysis. There was a small amount of variation at the facility level and no variation among geographic regions that was independent of patient- and facility-level factors.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".