Female Risk Factors for Post-Infarction Depression and Anxiety: Trial Design
Bibliographic record
Abstract
INTRODUCTION: Female patients are at elevated risk for adverse mental health outcomes following hospital admission for ischemic heart disease. These psychosocial characteristics are correlated with unacceptably higher rates of cardiovascular (CV) morbidity and mortality. Guidelines to address mental health following acute coronary syndrome (ACS) can only be developed with the aid of studies elucidating which subgroups of female patients are at the highest risk. METHODS/DESIGN: The Female Risk factors for post-Infarction Depression and Anxiety (FRIDA) Study is a prospective multicenter questionnaire-based study of female participants admitted to hospital with ACS. Data are collected within 72 h of admission as well as at 3 and 6 months. At baseline, participants complete a sociodemographic questionnaire, social support survey, and Hospital Depression and Anxiety Scale (HADS). Follow-up will consist of a demographic questionnaire, HADS, changes to health status, and quality of life indicators. Statistical analysis will include descriptive and inferential methods to observe baseline distributions and significance between groups. DISCUSSION/CONCLUSION: Our primary outcome is to determine if specific CV and sociodemographic factors correlate with increased depression and anxiety scores (HADS-D >7; HADS-A >7) at baseline. Our secondary aim is to determine if increased HADS scores at baseline and follow-up correlate with 3 and 6-month health and quality of life outcomes. A total of 2,000 patients will be enrolled across seven study sites. The aim of the FRIDA Study is to understand which groups of female patients have the highest rates of depression and anxiety following ACS to better inform care.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".