The predictive value of bedside ultrasound to restore spontaneous circulation in patients with pulseless electrical activity: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The prognosis of pulseless electrical activity is dismal. However, it is still challengable to decide when to terminate or continue resuscitation efforts. The aim of this study was to determine whether the use of bedside ultrasound (US) could predict the restoration of spontaneous circulation (ROSC) in patients with pulseless electrical activity (PEA) through the identification of cardiac activity. METHODS: This was a systematic review and meta-analysis of studies that used US to predict ROSC. A search of electronic databases (Cochrane Central, MEDLINE, EMBASE) was conducted up to June 2017, and the assessment of study quality was performed with the Newcastle-Ottawa Scale. Statistical analysis was performed with Review Manager 5.3 and Stata 12. RESULTS: Eleven studies that enrolled a total of 777 PEA patients were included. A total of 230 patients experienced ROSC. Of these, 188 had sonographically identified cardiac activity (pseudo-PEA). A meta-analysis showed that PEA patients with cardiac activity on US were more likely to obtain ROSC compared to those with cardiac standstill: risk ratio (RR) = 4.35 (95% confidence interval [CI], 2.20-8.63; p<0,00001) with significant statistical heterogeneity (I2 = 60%). Subgroup analyses were conducted: US evaluation using only on the subxiphoid view: RR = 1.99 (95% CI, 0.79-5.02; p = 0.15); evaluation using various views: RR = 4.09 (95% CI,2.70-6.02; p<0.00001). CONCLUSIONS: In cardiac arrest patients who present with PEA, bedside US has an important role in predicting ROSC. The presence of cardiac activity in PEA patients may encourage more aggressive resuscitation.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".