Predictors of cardiac symptom attribution among AMI patients.
Bibliographic record
Abstract
BACKGROUND: Care-seeking delay represents a major cause of death and disability for cardiac patients. With more than 70,000 new and recurrent acute myocardial infarctions (AMI) in Canada each year, recognizing symptoms as heart-related and seeking prompt medical care is essential for increasing the likelihood of successful treatment and survival. However, little is known about the factors associated with whether or not individuals attribute their symptoms to the heart (i.e., adopt a cardiac symptom attribution). PURPOSE AND DESIGN: Secondary analyses were conducted on data from a sample of 135 patients from four North American hospitals to identify the predictors of correct symptom attribution (CSA) during AMI. RESULTS AND CONCLUSIONS: Logistic regression investigations revealed that patients with a prior diagnosis of coronary heart disease and patients whose AMI experience paralleled their pre-existing symptom expectations were associated with greater odds of adopting a CSA. Results suggest that patient education and a clearer understanding of patients' beliefs about AMI can help nurses in acute care and community settings identify and manage misconceptions that may interfere with correctly attributing symptoms to a cardiac cause.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.008 |
| 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 | 0.001 |
| 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".