Impact of Predialysis Care on Clinical Outcomes
Bibliographic record
Abstract
INTRODUCTION: A structured predialysis multidisciplinary team program is beneficial in improving quality of life in patients with end-stage renal disease (ESRD). Educating pre-ESRD patients about their disease is vital in their care. Patients who can identify signs and symptoms of impending problems can seek help and avoid complications that may lead to hospital admissions. Our dialysis center offers two predialysis classes in a structured format. The first class is for those patients with mild to moderate renal disease, whereas the second class is for those with advanced renal disease who are expected to need dialysis in 3 to 6 months. The patients are followed by a multidisciplinary team once they are enrolled in our chronic kidney disease program. METHODS: We retrospectively reviewed all the charts of patients who started dialysis at our center between 1997 and 2000. We identified 68 patients who participated in the predialysis education program and 35 patients who did not because of late referral or refusal to participate. We compared these two groups over a 100-day period (10 days before initial dialysis and 90 days after), for hospitalizations, emergency room (ER) visits, and dialysis access placement. Patients' comorbid conditions, complications, and length of hospitalizations were extracted from the medical records. RESULTS: The 68 patients who completed the predialysis program had an average age of 60.3 years, a total of 96 hospital days, and 39 ER visits. Average length of hospital stay for these patients was 1.4 days. Three patients (4.4%) required placement of temporary catheters for the initial dialysis. Fifty-one percent of these patients had diabetes mellitus. The 35 patients of average age of 54.9 years who did not go through the program had 347 total hospital days and 39 ER visits. Average length of hospitalization was 9.9 days. Thirteen patients (37%) required temporary catheters for initial dialysis. This group included 16 patients (45.7%) with diabetes. CONCLUSION: Patients who participated in a multidisciplinary predialysis education program had fewer complications, ER visits, and hospitalizations. They also had fewer temporary catheter placements, shorter hospital stays, and reduced costs associated with initial dialysis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".