Association between change in physician remuneration and use of peritoneal dialysis: a population-based cohort analysis
Bibliographic record
Abstract
BACKGROUND: Health care payers are interested in policy-level interventions to increase peritoneal dialysis use in end-stage renal disease. We examined whether increases in physician remuneration for peritoneal dialysis were associated with greater peritoneal dialysis use. METHODS: We studied a cohort of patients in Alberta who started long-term dialysis with at least 90 days of preceding nephrologist care between Jan. 1, 2001, and Dec. 31, 2014. We compared peritoneal dialysis use 90 days after dialysis initiation in patients cared for by fee-for-service nephrologists and those cared for by salaried nephrologists before and after weekly peritoneal dialysis remuneration increased from $0 to $32 (fee change 1, Apr. 1, 2002), $49 to $71 (fee change 2, Apr. 1, 2007), and $71 to $135 (fee change 3, Apr. 1, 2009). Remuneration for peritoneal dialysis remained less than hemodialysis until fee change 3. We performed a patient-level differences-in-differences logistic regression, adjusted for demographic characteristics and comorbidities, as well as an unadjusted interrupted time-series analysis of monthly outcome data. RESULTS: Our cohort included 4262 patients. There was no statistical evidence of a difference in the adjusted differences-indifferences estimator following fee change 1 (0.89, 95% confidence interval [CI] 0.44-1.81), 2 (1.15, 95% CI 0.73-1.83), or 3 (1.52, 95% CI 0.96-2.40). There was no significant difference in the immediate change or the trend over time in peritoneal dialysis use between fee-for-service and salaried groups following any of the fee changes in the interrupted time-series analysis. INTERPRETATION: We identified no statistical evidence of an increase in peritoneal dialysis use following increased fee-for-service remuneration for peritoneal dialysis. It remains unclear what role, if any, physician payment plays in selection of dialysis modality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".