Comparative effectiveness of prophylactic therapies for necrotizing enterocolitis in preterm infants: Protocol for a network meta-analysis of randomized trials
Bibliographic record
Abstract
Necrotizing enterocolitis (NEC) is a common and devastating disease with high morbidity and mortality in premature infants. Current literature on the prevention of NEC has limitations including lack of direct and indirect comparisons of available therapies. We will search MEDLINE, EMBASE, Science Citation Index Expanded, Social Sciences Citation Index, CINAHL, Scopus, ProQuest Dissertations and Theses database, and grey literature sources to identify eligible trials evaluating NEC preventive therapies. Eligible studies will (1) enroll preterm (gestational age <37 weeks) and/or low birth weight (birth weight <2500 g) infants, (2) randomize infants to any preventive intervention or a placebo, or alternative active or nonactive intervention. Our outcomes of interest are severe NEC (stage II or more, based on Bell's criteria), all-cause mortality, NEC-related mortality, late-onset sepsis, duration of hospitalization, weight gain, time to establish full enteral feeds, and treatment-related adverse events. Two reviewers will independently screen trials for eligibility, assess risk of bias, and extract data. All discrepancies will be resolved by discussion. We will specify a priori explanations for heterogeneity between studies. For available comparisons between treatment and no treatment, and direct comparisons of treatments, we will conduct conventional meta-analysis using a random effects model. We will conduct a network meta-analysis using a random effects model within the Bayesian framework using Markov chain Monte Carlo methods to assess relative effects of eligible interventions. We will assess the certainty in direct, indirect, and network estimates using the Grading of Recommendations Assessment, Development and Evaluation approach. Ethics and Dissemination: We will disseminate our findings through a peer-reviewed publication and conference presentations.
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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 | 0.088 | 0.157 |
| Meta-epidemiology (narrow) | 0.009 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.056 | 0.006 |
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".