Prodromal symptoms associated with acute coronary syndrome acute symptom presentation
Bibliographic record
Abstract
OBJECTIVE: Prodromal symptoms (PS), indicative of myocardial ischemia, are frequently unrecognized by individuals prior to an acute coronary syndrome (ACS). ACSs are the leading cause of death worldwide. This study describes (1) the prevalence and association of PS with patients' baseline ACS-related acute symptoms of pain intensity and state anxiety and (2) the relationship of PS to co-morbidity. METHODS: An exploratory sub-analysis was performed. Cross sectional data identified prodromal predictors of ACS pain intensity (numeric rating scale 0-10 (NRS)) and state anxiety (Speilberger state-trait anxiety personality inventory (STAI)). ACS patients (n=121) admitted to a community rural emergency department completed the prodromal symptom screening scale (PS-SS) and reported baseline cardiac pain intensity, state, and trait anxiety. RESULTS: Increased ACS pain intensity was associated with PS. Median pain scores were higher by two points for those with prodromal headache, p=0.006, and anxiety, p=0.017, and one point higher for those with sleep disturbances, p=0.012. PS were not associated with state or trait anxiety. Hypertensive individuals were 7.5 times more likely to experience prodromal fatigue prior to their ACS event. CONCLUSION: Results extend current knowledge of the predictive value that prodromal headache, sleep disturbance and anxiety may have on individuals' acute symptom presentation. A prospective, prognostic study is required in order to determine whether PS are predictive of adverse cardiac events and if PS are a stronger predictor of ACS acute symptom presentation, compared with typical ACS-related co-morbidities.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".