Does Frequency of ST-Segment Elevation Myocardial Infarction Presentation Impact Quality of Care?
Bibliographic record
Abstract
Objectives The volume of ST-Segment Elevation Myocardial Infarctions (STEMIs) presenting to an emergency department (ED) has been shown to affect treatment quality measures and patient outcomes. Almost half of ST-elevation-myocardial-infarction (STEMI) patients in New Brunswick (NB) present directly to community hospitals. This study seeks to determine if the quality of care received by STEMI patients presenting to EDs in NB is related to the volume of STEMI presentations at that center. Methods This retrospective registry-based study used data from the STEMI database at the New Brunswick Heart Centre (NBHC), identifying 1196 cases of STEMI in NB, Canada, between December 2010 and April 2013. Patients were stratified into three groups based on the annual volume of STEMIs seen at the presenting center. Quality of care determinants, consisting of the percent of cases adhering to door-to-ECG (D2E), ECG-to-needle (E2N), and door-to-needle (D2N) time guidelines were then compared between groups. Results The mean age of the 1188 cases identified was 61.3 years, 73.8% were male, and 69.0% received thrombolysis. There was no difference in the rate of guideline adherence between the high, medium, and low-volume centers. The total rates of guideline adherence were 43.7%, 44.9%, and 47.5% for the D2E, E2N, and D2N times, respectively. Conclusion We did not identify any relationship between the rates of adherence with STEMI care guidelines and the volume of STEMI patients presenting to a center. Adherence rates were lower than in previously reported series from other regions. Further efforts should be undertaken to identify the causes of delayed STEMI diagnosis and treatment in our population and to implement system changes to improve standards of care.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".