Acurácia da Cronologia do Bloqueio de Ramo Esquerdo e dos Critérios Eletrocardiográficos para o Diagnóstico de Infarto Agudo do Miocárdio: Revisão Sistemática e Metanálise
Bibliographic record
Abstract
BACKGROUND: The diagnostic utility of new or presumed new left bundle branch block (LBBB) for acute myocardial infarction (AMI) in the setting of acute coronary syndrome (ACS) remains controversial. OBJECTIVE: To evaluate whether the timing of LBBB predicts AMI and to compare its diagnostic accuracy with ischemic electrocardiography (ECG) criteria, particularly the Modified Sgarbossa Criteria (MSC). METHODS: We searched PubMed and Scopus for studies involving patients with ACS with LBBB through December 2023. Sensitivity, specificity, positive (LR+) and negative (LR-) likelihood ratios, and diagnostic odds ratios (DOR) were calculated to assess diagnostic accuracy. Incidence and mortality data were also analyzed. Risk of bias was evaluated using the Newcastle-Ottawa Scale (NOS) and the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. RESULTS: A total of 51 studies were included. LBBB occurred in 3.3% of ACS presentations and was associated with higher in-hospital mortality. Differentiating new from old LBBB was diagnostically neutral: LR+ 1.30 (95% CI: 0.75 to 1.85), LR- 0.90 (95% CI: 0.79 to 1.02), and DOR 1.44 (95% CI: 0.93 to 2.24); all confidence intervals crossed the null value of 1.0. In contrast, MSC demonstrated 83.6% sensitivity (95% CI: 55.4 to 95.5%) and 92.6% specificity (95% CI: 78.9 to 97.7%) for angiographically confirmed occlusive AMI, with LR+ 11.34 (95% CI: 3.67 to 34.99) and LR- 0.18 (95% CI: 0.054 to 0.575). CONCLUSION: LBBB chronology alone does not significantly impact the likelihood of AMI. Ischemic ECG criteria - especially the MSC - provide substantially greater diagnostic accuracy and should guide clinical decision-making in ACS patients with LBBB.
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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 | 0.031 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.021 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".