Systemic resilience: COVID-19 and healthcare quality in acute myocardial infarction at tertiary medical centers – a nationwide study from Taiwan
Bibliographic record
Abstract
Background: The COVID-19 was a pandemic once in a century. This study aimed to review the quality of acute myocardial infarction (AMI) care during the COVID-19 pandemic from a national perspective. Methods: A retrospective analysis was conducted using Joint Commission of Taiwan (JCT)’s national Taiwan Clinical Performance Indicator (TCPI) system AMI care data, in correlation with Taiwan Centers for Disease Control COVID-19 epidemiological information. The time frames of the current study were categorized into four periods, based on the four levels of epidemic prevention and response established by the Central Epidemic Command Center (CECC). Quality indicators among different periods were compared and Statistical Process Control (SPC) charts were illustrated. Results: The incidences of AMI remained stable along the pandemic years. However, there was a significant drop of ST-segment elevated MI cases. The percutaneous coronary intervention performance (< 90 minutes upon arrival) for northern hospitals was significantly affected (second quarter of 2021) initially yet quickly recovered after second quarter of 2022. The in-hospital mortality of AMI patients was lower during the COVID-19 pandemic, especially in southern Taiwan. The CECC endeavored to secure healthcare capacity and continuously adapted strategies during the pandemic. Conclusion: Systemic resilience in healthcare systems is important as it will facilitate efforts to cope with emerging contagious diseases in the future. Interjurisdictional coordination including public health sectors, human service department, and healthcare systems is essential as well as adequate funding to maintain systemic resilience and better preparation for forthcoming events. Keywords: Resilience, COVID-19, Myocardial Infarction, Quality of Health 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".