Effective Adherence Contest to Improve Albumin, Phosphorus, and Fluid Levels in Pediatric Hemodialysis Unit
Bibliographic record
Abstract
Low serum albumin, high serum phosphorus, and fluid overload are common issues in dialysis patients. This can be attributed to many causes such as inadequate understanding and lack of accountability in the patients' care. These abnormal levels contribute to increased medical complications and increased mortality. Objective: (i) Improve patient education of albumin, phosphorus, and fluid maintenance. (ii) Improve patients' albumin, phosphorus, and fluid levels by 25 percent. Methods: A baseline level was collected on all patients by averaging last 3 laboratory findings. All patients were educated recognizing several different learning styles. Educational posters were displayed, one‐on‐one education was provided, as well as educational games on the role of albumin, phosphorus, and fluid. Patients were also educated on the role of diet in these levels. Positive reinforcement, peer pressure, and intensive team approach were used through the 8‐week incentive contest. Feedback on progress was provided in written and verbal format. Prizes were awarded for best shift levels and shift with most improvements. Results: Levels measured at the end of the educational period and contest showed a 71% improvement in albumin levels, 52% improvement in phosphorus levels, and a 42% improvement in fluid levels. Conclusion: Significant improvements found in all areas are attributed to three factors: education, consistent, individual, intensive attention, and incentives. Peer pressure was found not to be as effective but that individual tracking for incentives may be more effective if done ongoing. Our follow up several months later found a slight decrease in improvements, and we recognize a need for an ongoing intervention. Yet, we found an overall improvement of level of understanding and commitment to their overall health.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".