An exploration of proactive health oriented symptom patterns in patients undergoing percutaneous coronary intervention with stent implantation: A mixed-methods study protocol
Bibliographic record
Abstract
BACKGROUND: Coronary Heart Disease (CHD) is one of the most prevalent chronic diseases worldwide. Currently, percutaneous coronary intervention (PCI) with stent implantation is the main clinical treatment for CHD, and patients can achieve better outcomes after stenting. However, adverse cardiovascular events continue to recur, ultimately failing to yield good results. Several symptoms exist after stenting and are associated with health outcomes. Little is known about the symptom patterns of patients during the different postoperative periods. Therefore, this study aims to explore the dynamics of symptoms and clarify the experiences of post-stenting in patients during different periods, which may help the delivery of more specific patient management and improve survival outcomes in the future. METHODS: A mixed method (quantitative/qualitative) design will be adopted. Longitudinal research, including surveys regarding three different periods, will be sued to describe the symptom patterns of patients undergoing PCI with stent implantation, clarifying their focused symptom problems during different time periods or in populations with different features. Qualitative individual interviews aim to understand the feelings, experiences, opinions, and health conditions of patients post-stenting, which can explain and supplement quantitative data. Quantitative data will be analyzed using descriptive statistics, latent class analysis (LCA), and latent translation analysis (LTA). Qualitative data will be analyzed using content analysis. DISCUSSION: This study is the first study to explore the symptom patterns and experiences of patients in various domains after stent implantation using a novel design including quantitative and qualitative methods, which will help the delivery of more specific patient management, reduce the recurrence of adverse cardiovascular events, and improve survival outcomes in the future. It is also meaningful to use PROMIS profile-57 to help patients to proactively focus on their health problems, promote health literacy, and incorporate active patient participation into health management, which is a successful transition from passive medical treatment to active management.
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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 | 0.032 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".