Impact of community-based education on health care evaluation in patients with acute chest pain syndromes: the Wabasha Heart Attack Team (WHAT) project
Bibliographic record
Abstract
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".