Evaluation of chronic kidney disease symptom management algorithms/guidelines and patient information sheets in two Kidney Care Clinics
Bibliographic record
Abstract
Background: As renal function declines, symptoms related to chronic kidney disease (CKD) become more prevalent and impact quality of life. To address symptom management systematically in patients with eGFR < 15 mL/min not on dialysis, the British Columbia Provincial Renal Agency (BCPRA) developed 8 symptom management algorithms and patient information sheets. The objectives of this project are to assess patientsu2019 symptom burden before and after implementation of these tools and to assess patient and staff satisfaction. Methods: We conducted a prospective quantitative and qualitative study at 2 Kidney Care Clinics (KCCu2019s ). Five patients who were followed with the symptom management algorithms for nausea, low appetite, pruritis, and fatigue/insomnia were interviewed to assess satisfaction with care received via the algorithms and patient information sheets. Symptom burden was assessed pre and post algorithm use using a validated symptom assessment tool (Edmonton Symptoms Assessment Score (ESAS)). Next, focus groups with renal nurses (RNs) and renal dieticians (RDs) were conducted to assess provider satisfaction with the tools. Results: Following assessment of 5 patients, ESAS score improved for 4 patients after a mean (SD) follow up of 80 (13) days.Patients reported that recommendations provided were somewhat helpful for symptom and quality of life improvement and that the patient information sheets were helpful and easy to use. Of the 13 RNs and RDs in the focus groups, 12/13 were satisfied or very satisfied with the algorithms and 13/13 were satisfied or very satisfied with the patient information sheets. Major themes identified by patients and providers included their satisfaction with the ability to improve standardized care, patient education, patient-centered care, accountability, and follow-up.Conclusions: Following implementation of BCRA symptom management algorithms/guidelines, we were able to show a reduction in symptoms for patients with a GFR < 15 mL/min not on dialysis using validated symptom management tools. Patients and providers found the information sheets/algorithms helpful and easy to use.
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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 | 0.022 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".