The role of nurse practitioners in primary care in optimizing risk stratification for coronary heart disease in Canadian women: an integrative review
Bibliographic record
Abstract
Coronary heart disease (CHD) is the most common cause of morbidity and mortality in Canadian women.Despite advances in screening and research, CHD continues to pose a significant health care burden to Canadian women.This integrative literature review explores how a Nurse Practitioner (NP) in primary care can optimize risk stratification for CHD in Canadian women.A systematic search of the contemporary literature identified 11 key articles.These were analyzed using the Critical Appraisal Skills Programme tools to assess relevance and the strengths and weaknesses of the evidence.Three key themes emerged from the literature and are explored in detail: the limitations of current risk prediction models for risk stratification in women; the emergence and evolving importance of female-specific risk factors; and additional adjunctive testing (coronary artery calcium screening) that may improve the accuracy of risk prediction models in women.Recommendations based on the above themes with respect to NP practice, education, and research are identified.Female-specific risk stratification, improving NP education, and areas for further research including the need for screening beyond traditional risk prediction models are highlighted.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".