Predictors of Urgent Dialysis and Hospitalization Following Ambulance Transport to the Emergency Department
Bibliographic record
Abstract
Background: Dialysis patients may require timely, monitored dialysis (urgent dialysis) or hospitalization after ambulance transport to the emergency department (ambulance-ED). We developed and internally validated risk prediction models for urgent dialysis and hospitalization for ambulance-ED in a cohort of chronic dialysis patients. Methods: We included all ambulance-ED transports for hemodialysis patients affiliated with a large regional program from 2014-2018. “Urgent dialysis” was defined as dialysis within 24 hours of ED arrival in a monitored setting or with the first ED patient blood potassium level >6.5mmol/L. Predictors included categorized vital signs prior to ambulance transport (taken by paramedics) and time from last dialysis. Logistic regression models were used to predict urgent dialysis and hospitalization and internally validated using bootstrapping. Model discrimination was evaluated using the C-statistic and calibration using the Hosmer-Lemeshow test. Results: A total of 271 dialysis patients experienced 878 ambulance-ED transports. 63 transports (7.2%) required urgent dialysis and 299 (34.0%) resulted in hospitalization. Hypoxemia (odds ratio; OR: 4.04, 95% CI: 1.75-9.33) and a time from last dialysis of 24-48 hours (OR: 3.43, 95% CI: 1.05-11.9) and >48 hours (OR: 9.22, 95% CI: 3.37-25.23) were associated with urgent dialysis. A risk prediction model for urgent dialysis had good discrimination (C-statistic: 0.83) and calibration (Hosmer-Lemeshow: 0.89). The prediction model for hospitalization had no individually significant predictors of hospitalization and moderate discrimination (C-statistic: 0.67) but good calibration (Hosmer-Lemeshow: 0.74). Conclusions: This study highlights the possibility of predicting certain shortterm outcomes in dialysis patients using information available to paramedics during ambulance-ED transport.
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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 | 0.001 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".