Effect of different pre-hospital first aid methods on the efficacy and prognosis of acute myocardial infarction with left heart failure: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Pre-hospital first aid for acute myocardial infarction (AMI) is an important way to save patients. However, there are still some disputes about the way of pre-hospital first aid. Therefore, this paper provides a Meta-analysis to evaluate the efficacy and prognosis of different prehospital care for AMI with left heart failure. Methods: By searching the published studies in the databases, the literature related to the pre-hospital first aid for patients with AMI and left heart failure was screened out. The quality of the literature was evaluated according to the Newcastle-Ottawa scale (NOS), and the corresponding data were extracted for meta-analysis. Meta-analysis was performed on 7 outcome indicators (clinical effect of patients after treatment, respiratory rate, heart rate, systolic blood pressure (SBP), diastolic blood pressure (DBP), survival status, and incidence of complications). A funnel plot and Egger's test were used to test risk of bias. Results: A total of 16 articles were finally included, comprising a total of 1,465 patients. The literature quality evaluation found that 8 literatures were rated as low risk of bias, and 8 literatures were rated as medium risk of bias. The meta-analysis results showed that the clinical effect of the first aid and then transportation group was better than that of the transportation and then first aid group [risk ratio (RR) =1.35, 95% confidence interval (CI): 1.27 to 1.45, P<0.01]; the respiratory rate decreased [mean difference (MD) =-4.84, 95% CI: -6.50 to -3.18, P<0.01]; the heart rate decreased (MD =-11.34, 95% CI: -12.69 to -9.99, P<0.01); SBP decreased (MD =-6.00, 95% CI: -10.00 to -2.00, P<0.01); the DBP decreased (MD =-3.54, 95% CI: -4.45 to -2.64, P<0.01); the survival status of the patients improved (RR =1.29, 95% CI: 1.18 to 1.41, P<0.01); the incidence of complications was reduced (RR =0.31, 95% CI: 0.20 to 0.48, P<0.01). Conclusions: Pre-hospital first aid and then transportation can significantly improve the clinical treatment effect of patients. However, considering that the literatures included in this paper are non-randomized controlled studies and the overall quality of the included literatures is not high and the number of studies is limited, further exploration is needed.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.049 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".