Anticoagulation regimens during pregnancy in patients with mechanical heart valves: a protocol for a systematic review and network meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Pregnancy in patients with mechanical heart valves (MHVs) is associated with high maternal complications and fetal complications.Anticoagulation treatments serve to decrease their venous clotting risk. Although some anticoagulation regimens have been used for patients during pregnancy with MHVs, no one is definitively superior among different regimens in recent studies. For a better understanding of the clinical treatment which anticoagulation regimen is more effective and safer during the pregnancy in patients with MHVs, a Bayesian network meta-analysis is necessary. METHODS AND ANALYSIS: This protocol has been reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols. Related studies until April 2019 will be searched in the following databases: PubMed, Embase,SinoMed and the using the OVID interface to search for evidence-based medicine reviews. A clinical trial registry (www.ClinicalTrials.gov) was also searched for unpublished trials. Both experimental studies (randomised clinical trials) and observational studies (cohort studies, case-control studies and case series studies) will be included in this study. Quality assessment will be conducted using Cochrane Collaboration's tool or Newcastle-Ottawa Scale based on their study designs. The primary outcomes of interest will be the frequencies of serious maternal and fetal events. The additional outcomes of interest will be adverse maternal events, mode of delivery and adverse fetal events. Pairwise and network meta-analysis will be conducted using R (V.3.4.4, R Foundation for Statistical Computing, Vienna, Austria) and Stata (V.14, StataCorp). The ranking probabilities will be estimated at each possible rank for each anticoagulation regimen using the surface under the cumulative ranking curve. Statistical inconsistency assessment, subgroup analysis, sensitivity analysis and publication bias assessment will be performed. ETHICS AND DISSEMINATION: Either ethics approval or patient consent is not necessary, because this study will be based on literature. The results of this study will be published in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42019130659.
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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 | 0.056 | 0.098 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.050 | 0.004 |
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".