Comparative efficacy of volume expansion, inotropes and vasopressors in preterm neonates with probable transitional circulatory instability in the first week of life: a systematic review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: July 2023. Two authors extracted the data independently. A Bayesian random effects network meta-analysis was used. Recommendations were formulated using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) framework. INTERVENTIONS: Dopamine, dobutamine, epinephrine, hydrocortisone, vasopressin, milrinone, volume and placebo. MAIN OUTCOME MEASURES: Mortality, major brain injury (MBI) (intraventricular haemorrhage > grade 2 or cystic periventricular leukomalacia), necrotising enterocolitis (NEC) ≥stage 2 and treatment response (as defined by the author). RESULTS: 15 Randomized Controlled Trials (RCTs) were included from the 1365 titles and abstracts screened. Clinical benefit or harm could not be ruled out for the critical outcome of mortality. For the outcome of MBI, epinephrine possibly decreased the risk when compared to dobutamine and milrinone (very low certainty). Epinephrine was possibly associated with a lesser risk of NEC when compared with dopamine, dobutamine, hydrocortisone and milrinone (very low certainty). Dopamine was possibly associated with a lesser risk of NEC when compared with dobutamine (very low certainty). Vasopressin possibly decreased the risk of NEC compared with dopamine, dobutamine, hydrocortisone and milrinone (very low certainty). Clinical benefit or harm could not be ruled out for the outcome response to treatment. CONCLUSIONS: Epinephrine may be used as the first-line drug in preterm neonates with TCI, the evidence certainty being very low. We suggest future trials evaluating the management of TCI with an emphasis on objective criteria to define it.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.031 |
| Bibliometrics | 0.006 | 0.005 |
| 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) | 0.004 | 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".