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Record W4362518817 · doi:10.21037/jtd-23-195

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

2023· review· en· W4362518817 on OpenAlexaboutno aff
Tianmei Lin, Xiaomei Chen, Qiongyue Wu, Lijiao Zou, Shuhong Wu

Bibliographic record

VenueJournal of Thoracic Disease · 2023
Typereview
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeta-analysisFunnel plotMyocardial infarctionPublication biasConfidence intervalHeart failureInternal medicineBlood pressureRelative riskCardiology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.033
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0200.049
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.031
GPT teacher head0.407
Teacher spread0.376 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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