Epidemiology of nursing home dialysis patients—A hidden population
Bibliographic record
Abstract
INTRODUCTION: Dialysis patients are often discharged from hospitals to skilled nursing facilities (SNFs), but little has been published about their natural history. METHODS: Using electronic medical record data, we conducted a retrospective cohort study of nursing home patients treated with in-SNF hemodialysis from January 1, 2018 through June 20, 2020 within a dialysis organization across eight states. A dialytic episode began with the first in-SNF dialysis and was ended by hospitalization, death, transfer, or cessation of treatment. The clinical characteristics and natural history of these patients and their dialytic episodes are described. FINDINGS: Four thousand five hundred and ten patients experienced 9274 dialytic episodes. Dialytic episodes had a median duration of 18 days (IQR: 8-38) and were terminated by a hospitalization n = 5747 (62%), transfer n = 2638 (28%), death n = 568 (6%), dialysis withdrawal n = 129 (1.4%), recovered function n = 2 (0.02%), or other cause n = 6 (0.06%). Increased patient mortality was associated with advancing age, low serum creatinine, albumin, or sodium, and low pre-dialytic systolic blood pressure (sBP). U-shaped relationships to mortality were observed for intradialytic hypotension frequency and for post- > pre-hemodialysis sBP frequency. Prescription of dialysis five times weekly in the first 2 weeks was associated with better survival in the first 90 days (HR 0.77, CI 0.62-0.96; p < 0.02). DISCUSSION: Provision of in-SNF dialysis by an external dialysis organization enables discharge from the acute care setting for appropriate treatment with increased nursing contact time in an otherwise under-resourced environment. SNF ESRD patient clinical characteristics and outcomes are extensively characterized for the first time.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 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".