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The feasibility and effectiveness of emergency department based hypertension screening: A systematic review

2011· review· en· W1829823427 on OpenAlexaff
Roger L. Scott, Garnet Cummings, Christine Newburn‐Cook

Bibliographic record

VenueJournal of the American Academy of Nurse Practitioners · 2011
Typereview
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineEmergency departmentCINAHLReferralMEDLINEScopusPopulationBlood pressureCochrane LibraryFamily medicinePediatricsEmergency medicineMeta-analysisPsychological interventionInternal medicineNursing

Abstract

fetched live from OpenAlex

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 DATA SOURCES: An online search of databases (i.e., OVID Search, CINAHL, Scopus, Web of Science), unpublished sources (i.e., ProQuest Dissertation & Theses and Papers First), and grey literature (i.e., OpenSIGLE and the New York Academy of Grey Literature) was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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.200
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.125
GPT teacher head0.402
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations10
Published2011
Admission routes1
Has abstractyes

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