6 Month Experience with 1 Nurse Training 2 Patients Together for Nightly Home Hemodialysis
Bibliographic record
Abstract
Objective: To have 1 nurse successfully train 2 patients at a time for nightly home hemodialysis (NHHD) within a 7–10 week time period. Methods: Over the past 6 years, Lynchburg Nephrology Dialysis Inc. has evolved its NHHD program from 1 nurse training 1 patient during an average 6 week period to training 2 patients in a 7–10 week period. Patients came either from our in‐center population or directly from internal medicine. Our pre‐evaluation procedures and manual were revised. Patients were pre‐evaluated for literacy, manual dexterity, strength, hearing and visual deficits, substance abuse, psychiatric disorders, and compliance before being accepted into the program. A home visit was made to evaluate their environment, family interactions, and water source. Our training manual was rewritten to fifth grade level. Every patient was given index cards printed in 20 size font with step‐by‐step procedures for machine setup, put‐on and takeoff, re‐circulation, and power failure. Patients were dialyzed 4 days/week to improve cognitive function and ‘dialyzed the bucket’ 1 day, thus training 5 days/week. One nurse trained 2 patients, staggering their start dates 2 weeks apart to allow for both individual and group teaching. Fistula or IJ catheter was used for access. Patients were trained alone or with a partner. In the last week of training, patients were dialyzed for 7 h and were expected to complete all procedures independently. Results: From March 2003 to August 2003, 6 patients were trained for NHHD. 2 patients finished training in 7 weeks, 2 in 9 weeks, and 2 in 10 weeks. Conclusion: One nurse can successfully train 2 patients at a time for NHHD in a 7–10 week time period at a decreased cost to dialysis provider.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".