Multiple antiarrhythmic transplacental treatments for fetal supraventricular tachyarrhythmia
Bibliographic record
Abstract
BACKGROUND: Fetal supraventricular tachyarrhythmia is a common reason for referral to fetal cardiology. Multiple antiarrhythmic transplacental medications can be used to treat these diseases. Debates remain regarding the standardized therapy. METHODS: PubMed, EMBASE, Cochrane Library, Web of Science, Google Scholar, and ClinicalTrials.gov will be searched from inception to September 2020. A handsearching for gray literature, including unpublished conference articles, will be performed. The randomized control trials, case-control, and cohort studies will be accepted, no matter what the languages they were reported. We will first focus on the effectiveness of the therapy on fetal cardiac rhythm and/or heart rate. Then we will do further analysis of preterm delivery, fetal hydrops, intrauterine fetal demise, and maternal side effects. The Cochrane Risk of Bias Tool and the Newcastle-Ottawa scale will be used to assess the risk of bias of the randomized controlled trials, case-control, and cohort studies, respectively. Two independent reviewers will carry out literature identification, data collection, and study quality assessment. Discrepancies will be resolved by a third reviewer. Statistical analysis will be conducted using the STATA 13.0 software. RESULT: The results will provide helpful information about the effect of multiple antiarrhythmic transplacental therapies in pregnancies with supraventricular tachycardia or atrial flutter, and demonstrate which therapy is more effective. CONCLUSION: The conclusion drawn from this systematic review will benefit the patients with fetal supraventricular tachyarrhythmia.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".