Re‐evaluation of re‐hospitalization and rehabilitation in renal research
Bibliographic record
Abstract
INTRODUCTION: The use of administrative data to capture 30-day readmission rates in end-stage renal disease is challenging since Medicare combines claims from acute care, inpatient rehabilitation (IRF), and long-term care hospital stays into a single "Inpatient" file. For data prior to 2012, the United States Renal Data System does not contain the variables necessary to easily identify different facility types, making it likely that prior studies have inaccurately estimated 30-day readmission rates. METHODS: For this report, we developed two methods (a "simple method" and a "rehabilitation-adjusted method") to identify acute care, IRF, and long-term care hospital stays from United States Renal Data System claims data, and compared them to methods used in previously published reports. FINDINGS: We found that prior methods overestimated 30-day readmission rates by up to 12.3% and overestimated average 30-day readmission costs by up to 11%. In contrast, the simple and rehabilitation-adjusted methods overestimated 30-day readmission rates by 0.1% and average 30-day readmission costs by 1.8%. The rehabilitation-adjusted method also accurately identified 96.8% of IRF stays. DISCUSSION: Prior research has likely provided inaccurate estimates of 30-day readmissions in patients undergoing dialysis. In the absence of data on specific facility types particularly when using data prior to 2012, future researchers could employ our method to more accurately characterize 30-day readmission rates and associated outcomes in patients with end-stage renal disease.
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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 | 0.094 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".