The feasibility and effectiveness of emergency department based hypertension screening: A systematic review
Bibliographic record
Abstract
PURPOSE: Hypertension is a highly prevalent risk factor for cardiovascular disease, and its early identification and management results in reductions in morbidity and mortality. Our objectives were to: (1) determine the extent to which the emergency department (ED) has been used to screen patients for undiagnosed hypertension; (2) estimate the incidence of undiagnosed hypertension in the ED population; (3) identify and describe the programs for ED hypertension screening; and (4) determine the feasibility of ED-based hypertension screening programs and the requirements for further study. DATA SOURCES: An online search of databases (i.e., OVID Search, CINAHL, Scopus, Web of Science), unpublished sources (i.e., ProQuest Dissertation & Theses and Papers First), and grey literature (i.e., OpenSIGLE and the New York Academy of Grey Literature) was conducted. A manual search of the reference lists of relevant studies was also completed. CONCLUSION: Hypertension screening in the ED is feasible. Individuals with elevated blood pressure (BP) in the ED should be referred for follow-up. Further study is needed to develop an ED screening tool that is predictive of persistently elevated BP in undiagnosed individuals. IMPLICATIONS FOR PRACTICE: Nurse practitioners in the ED should identify patients with elevated BP, provide hypertension education, and ensure appropriate intervention and referral.
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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 | 0.016 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".