Efficacy and safety of vasopressin and terlipressin in preterm neonates: a protocol for a systematic review
Bibliographic record
Abstract
Background: The use of vasoactive agents like arginine vasopressin (AVP) and terlipressin to treat hypotension or persistent pulmonary hypertension in critically ill preterm neonates is increasing. Therefore, a systematic review of the available data on dosing, efficacy and safety of AVP and terlipressin in this patient population appears beneficial. Methods: We will conduct a systematic review of the available evidence on the use of AVP and terlipressin for the treatment of hypotension or persistent pulmonary hypertension in preterm neonates. We will search Ovid MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, Web of Science and Google Scholar from inception to March 2021. Two reviewers will independently screen titles and abstracts, review the full text of eligible studies, extract data, assess the risk of bias and judge the certainty of the evidence. Our primary outcome will be an (1) improvement of end-organ perfusion after initiation of AVP or terlipressin and (2) mortality prior to discharge. Our secondary outcomes will include (1) major neurosensory abnormality and (2) the occurrence of adverse events. Discussion: The currently available evidence on the efficacy and safety of AVP and terlipressin in preterm neonates is limited. Yet, evidence on the pharmacology of these drugs and the pathophysiology of vasoplegic shock support the biological plausibility for their clinical effectiveness in this population. Therefore, we aim to address this gap concerning the use of vasopressin and terlipressin among critically ill preterm neonates. Trial registration: This protocol has been submitted for registration to the international database of prospectively registered systematic reviews (PROSPERO, awaiting registration number).
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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 | 0.053 | 0.076 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.018 | 0.019 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.005 |
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".