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Record W2030708307 · doi:10.1093/fampra/18.5.537

Impact of community-based education on health care evaluation in patients with acute chest pain syndromes: the Wabasha Heart Attack Team (WHAT) project

2001· article· en· W2030708307 on OpenAlexaff
R. Scott Wright, Stephen L. Kopecky, Maureen Timm, David Pflaum, Christina Carr, Kathryn Evers, J. Simon Bell

Bibliographic record

VenueFamily Practice · 2001
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsHealth Care Foundation
Fundersnot available
KeywordsMedicineChest painMyocardial infarctionEmergency departmentEmergency medicineEmergency medical servicesAcute coronary syndromeAcute painMedical emergencyMultidisciplinary teamPhysical therapyInternal medicineNursingAnesthesia

Abstract

fetched live from OpenAlex

BACKGROUND: Community education programmes focused on raising public awareness of the symptomatology of acute coronary syndromes have had mixed results. OBJECTIVES: The Wabasha Heart Attack Team project, a unique multidisciplinary public education effort in Minnesota, sought to educate area citizens about signs and symptoms of acute myocardial infarction (MI). METHODS: After an intensive 1-month education period, we compared presentations for emergency evaluation of chest pain during the study period with baseline data from the same seasonal period of the preceding year. RESULTS: Visits to the Emergency Room for symptomatic heart disease increased significantly during the study period (56 patients versus 46 patients during the baseline period), as did the percentage of patients presenting with acute MI (18% versus 12%, P < 0.05). Use of emergency medical services for pre-hospital evaluation was significantly increased (41% versus 27%, P < 0.05). CONCLUSION: A community education campaign can significantly increase use of pre-hospital emergency medical service resources and may increase the number of patients presenting with acute chest pain symptoms, including MI.

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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.100
GPT teacher head0.456
Teacher spread0.356 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations37
Published2001
Admission routes1
Has abstractyes

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