Patient and physician perspectives on shared decision-making for coronary procedures in people with chronic kidney disease: a patient-oriented qualitative study
Bibliographic record
Abstract
BACKGROUND: Patients with chronic kidney disease (CKD) and heart disease face challenging treatment decisions. We sought to explore the perceptions of patients and physicians about shared decision-making for coronary procedures for people with CKD, as well as opinions about strategies and tools to improve these decisions. METHODS: We partnered with 4 patients with CKD and 1 caregiver to design and conduct a qualitative descriptive study using semi-structured interviews and content analysis. Patient participants with CKD and either acute coronary syndrome or cardiac catheterization in the preceding year were recruited from a provincial cardiac registry, cardiology wards and clinics in Calgary between March and September 2018. Cardiologists from the region also participated in the study. Data analysis emphasized identifying, organizing and describing themes found within the data. RESULTS: Twenty patients with CKD and 10 cardiologists identified several complexities related to bidirectional information exchange needed for shared decision-making. Themes identified by both patients and physicians included challenges synthesizing best evidence, variable patient knowledge seeking, timeliness in the acute care setting and influence of roles on decision-making. Themes identified by physicians related to processes and tools to help support shared decision-making in this setting included personalization to reflect the variability of risks and heterogeneity of patient preferences as well as allowing for physicians to share their clinical judgment. INTERPRETATION: There are complexities related to bidirectional information exchange between patients with CKD and their physicians for shared decision-making about coronary procedures. Processes and tools to facilitate shared decision-making in this setting require personalization and need to be time sensitive.
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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 | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".