The effects of optimizing the timeliness of emergency care of patients with ST-elevation myocardial infarction in hospital
Bibliographic record
Abstract
Objective To investigate the effect of optimizing rescue time for patients with acute st-elevation myocardial infarction (STEMI) in the hospital. Methods A retrospective analysis of the clinical data of 133 patients with ST-elevation myocardial infarction who were hospitalized in the first affiliated hospital of university of science and technology of china during July,2016 to June,2017 was performed. Timeline in the rescue, the result of coronary reperfusion and satisfaction degree of patients were analyzed. Results The rapid evaluation time (F=2.609, P=0.046),emergency handling time(F=7.581, P=0.032), login and logout time (F=5.667, P=0.017)and visit-ballon time (F=8.942, P=0.007) were shortened quarter by quarter. The average time of each project in the four quarters showed a statistically significant difference. The difference of TIMI classification of coronary flow reperfusion among the four quarters was statistically significant (H=8.402, P=0.038). The satisfaction degree of each quarter showed a statistically significant difference (the third quarter of 2016:94.68±2.38, the fourth quarter of 2016:96.72±5.10, the first quarter of 2017: 97.23±7.64, the second quarter of 2017:98.36±4.86; F=7.891, P=0.048). Conclusions Enhancing timeliness of emergency care can remarkably shorten rescue time, improve satisfaction degree of patients and help to improve the success rate of emergency treatment for patients with STEMI. Key words: Acute ST segment elevation myocardial infarction; Nursing process; Hospital rescue; Timeliness
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".