Adjuvant therapy in neonatal sepsis to prevent mortality - A systematic review and network meta-analysis
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Despite appropriate antibiotic therapy, the risk of mortality in neonatal sepsis still remains high. We conducted a systematic review to comprehensively evaluate different adjuvant therapies in neonatal sepsis in a network meta-analysis. METHODS: We included randomized controlled trials (RCTs) and quasi-RCTs that evaluated adjuvant therapies in neonatal sepsis. Neonates of all gestational and postnatal ages, who were diagnosed with sepsis based on blood culture or sepsis screen were included. We searched MEDLINE, CENTRAL, EMBASE and CINAHL until 12th April 2021 and reference lists. Data extraction and risk of bias assessment were performed in duplicate. A network meta-analysis with bayesian random-effects model was used for data synthesis. Certainty of evidence (CoE) was assessed using GRADE. RESULTS: We included 45 studies involving 6,566 neonates. Moderate CoE showed IVIG [Relative Risk (RR); 95% Credible Interval (CrI): 1.00; (0.67-1.53)] as an adjunctive therapy probably does not reduce all-cause mortality before discharge, compared to standard care. Melatonin [0.12 (0-0.08)] and granulocyte transfusion [0.39 (0.19-0.76)] may reduce mortality before discharge, but CoE is very low. The evidence is also very uncertain regarding other adjunctive therapies to reduce mortality before discharge. Pentoxifylline may decrease the duration of hospital stay [Mean difference; 95% CrI: -7.48 days (-14.50-0.37)], but CoE is very low. CONCLUSION: Given the biological plausibility for possible efficacy of these adjuvant therapies and that the CoE from the available trials is very low to low except for IVIG, we need large adequately powered RCTs to evaluate these therapies in sepsis in neonates.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it