Spontaneous Coronary Artery Dissection Across the Health Care Pathway: A National, Multicenter, Patient‐Informed Investigation
Bibliographic record
Abstract
BACKGROUND: Clinical practice guidelines for the management and convalescence of patients with spontaneous coronary artery dissection (SCAD) have yet to be developed. The targeted content, delivery, and outcomes of interventions that benefit this population remain unclear. Patient-informed data are required to substantiate observational research and provide evidence to inform and standardize clinical activities. METHODS AND RESULTS: Patients diagnosed with SCAD (N=89; 86.5% women; mean age, 53.2 years) were purposively selected from 5 large tertiary care hospitals. Patients completed sociodemographic and medical questionnaires and participated in an interview using a patient-piloted semistructured interview guide. Interviews were transcribed and subjected to framework analysis using inductive and then deductive coding techniques. Approximately 1500 standard transcribed pages of interview data were collected. Emotional distress was the most commonly cited precipitating factor (56%), with an emphasis on anxiety symptoms. The awareness and detection of SCAD as a cardiac event was low among patients (35%) and perceived to be moderate among health care providers (55%). Health care providers' communication of the prognosis and self-management of SCAD were perceived to be poor (79%). Postevent psychological disorders among patients were evident (30%), and 73% feared recurrence. Short- and longer-term follow-up that was tailored to patients' needs was desired (72%). Secondary prevention programming was recommended, but there were low completion rates of conventional cardiac rehabilitation (48%), and current programming was deemed inadequate. CONCLUSIONS: This early-stage, pretrial research has important implications for the acute and long-term management of patients with SCAD. Additional work is required to validate the hypotheses generated from this patient-oriented research.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".