Code Status Variability in a Regional Hemodialysis Program
Bibliographic record
Abstract
Background: Patients with end stage kidney disease (ESKD) treated with hemodialysis (HD) have poor life expectancy and may not benefit from aggressive measures at the end of life. Previous studies suggest variability in Do Not Resuscitate (DNR) orders in patients treatd with HD but they are limited by missing code statuses and inability to adjust for demographics. In our regional HD program, with complete code status ascertainment that is updated annually, our objective was to examine DNR variability while accounting for demographic factors. Methods: We conducted a cross-sectional study of DNR prevalence in October 2019 in patients treated with in-centre HD in a regional program, which consists of a main centre and six smaller centers. Patients are transferred to smaller centres based on location. Each centre has an attending nephrologist who reviews code status yearly with every patient. Unadjusted DNR prevalance are compared using the Chi-square test and multiple logistic regression is used to control for covariates (age, sex, race, dialysis vintage, HD unit). Results: We included 374 patients, 193 (52%) from the main centre and 181 (48%) from its satelite units. Mean age of patients is 67.2±14.3 years, 52% male, 87% Caucasian, and dialysis vintage 5.2±5.5 years. Code status was full code in 78% and DNR in 22% with significant variation across sites (range of 9% to 44%, p = 0.02) (Figure 1). Variation remained significant (p = 0.03) after controlling for covariates.Figure 1:: Unadjusted DNR prevalence in hemodialysis units across a regional dialysis program.Conclusions: Variability in code status at different HD centres in our regional program persisted despite accounting for differences in patient age, sex, race, and HD vintage. This finding suggests factors related to the HD centre may affect code status decisions, such as local culture, question phrasing, and views of the treating nephrologist. Future studies are planned to determine if a standardized approach to discussing code status would normalize rates.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".