Evaluation of a collaborative chronic care approach to improve outcomes in kidney transplant recipients
Bibliographic record
Abstract
Several studies found that renal transplant recipients with chronic kidney disease have untreated complications and do not attain recommended clinical targets. Using a before/after design with propensity score-matched controls, we evaluated whether an advanced practice nurse-led interprofessional collaborative chronic care approach could improve clinical outcomes for CKD transplant patients compared with a traditional physician-led model. The intervention included strategies for disease self-management, shared decision making, and healthcare system reorganization. The primary outcome was the proportion of patients attaining at least seven of nine targets as per published guidelines. A greater proportion of intervention patients achieved the outcome (68% vs. 10%; p = 0.0001) and had discussions about end-stage treatment options (88% vs. 13%; p = 0.0001) compared with controls. The intervention patients had significantly fewer emergency room visits (incidence rate ratio [IRR] 0.53; 95% CI 0.29-0.91; p = 0.02) and hospital admissions (IRR 0.34; 95% CI 0.16-0.68; p = 0.001) compared with the control patients. There were no significant differences found between the groups in systolic/diastolic blood pressure, carbon dioxide, hemoglobin, or phosphate parameters. An advanced practice nurse-led approach, based on the chronic care model, has the potential to improve clinical outcomes for renal transplant recipients and needs to be tested in a multicenter randomized controlled trial.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".