Efficacy of left ventricular unloading strategies during venoarterial extracorporeal membrane oxygenation in patients with cardiogenic shock: a protocol for a systematic review and Bayesian network meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Venoarterial extracorporeal membrane oxygenation (VA-ECMO) has been widely used for patients with refractory cardiogenic shock. A common side effect of this technic is the resultant increase in left ventricular (LV) afterload which could potentially aggravate myocardial ischaemia, delay ventricular recovery and increase the risk of pulmonary congestion. Several LV unloading strategies have been proposed and implemented to mitigate these complications. However, it is still indistinct that which one is the best choice for clinical application. This Bayesian network meta-analysis (NMA) aims to compare the efficacy of different LV unloading strategies during VA-ECMO. METHODS AND ANALYSIS: PubMed, Embase, the Cochrane Library and the International Clinical Trials Registry Platform will be explored from their inception to 31 December 2020. Random controlled trials and cohort studies that compared different LV unloading strategies during VA-ECMO will be included in this study. The primary outcome will be in-hospital mortality. The secondary outcomes will include neurological complications, haemolysis, bleeding, limb ischaemia, renal failure, gastrointestinal complications, sepsis, duration of mechanical ventilation, length of intensive care unit and hospital stays. Pairwise and NMA will respectively be conducted using Stata (V.16, StataCorp) and Aggregate Data Drug Information System (V.1.16.5), and the cumulative probability will be used to rank the included LV unloading strategies. The risk of bias will be conducted using the Cochrane Collaboration's tool or Newcastle-Ottawa Quality Assessment Scale according to their study design. Subgroup analysis, sensitivity analysis and publication bias assessment will be performed. The Grading of Recommendations Assessment, Development and Evaluation will be conducted to explore the quality of evidence. ETHICS AND DISSEMINATION: Either ethics approval or patient consent is not necessary, because this study will be based on literature. The results will be disseminated through peer-reviewed publications and conference presentations. PROSPERO REGISTRATION NUMBER: CRD42020165093.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".