Patient Experience After Risk Stratification and Follow-up for Acute Kidney Injury After Cardiac Catheterization: Patient Survey
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) after cardiac catheterization procedures is associated with poor health outcomes. We sought to characterize the experiences of patients after receiving standardized information on their risk of AKI accompanied by instructions for follow-up care after cardiac catheterization. METHODS: We implemented an initiative across 3 cardiac catheterization units in Alberta, Canada to provide standardized assessment, followed by guidance for patients at risk of AKI. This was accompanied by communication to primary care providers to improve continuity of care when patients transition from the hospital to the community. A structured survey from a sample of 100 participants at increased risk of AKI determined their perceptions of information provided and experiences with follow-up steps after the initiative was implemented in each cardiac catheterization unit in Alberta. RESULTS: The mean age of participants was 72.4 (SD 10.4) years, 37% were female, and the mean risk of AKI was 8.8%. Most (63%) participants were able to recall the information provided to them about their risk of kidney injury, 68% recalled the education provided on strategies to reduce risk, and 65% believed their primary care practitioner had received enough information to conduct appropriate follow-up care. Eighty-six percent of patients were satisfied with their transition to the community, and 53% were reassured by the information and follow-up care they received. CONCLUSIONS: These findings suggest that communicating risk information to patients, in combination with education and collaboration for follow-up with primary care providers, is associated with positive patient experiences and satisfaction with care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".