Bovine Lactoferrin to Prevent Neonatal Infections in Low-Birth-Weight Newborns in Pakistan: Protocol for a Three-Arm Double-Blind Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Sepsis is a common and severe complication in premature neonates, particularly those born with low birth weights (<2500 g). Neonatal sepsis is steadily emerging as a leading cause of neonatal mortality in Pakistan. Lactoferrin is a natural product with broad-spectrum antimicrobial properties and glycoprotein that is actively involved in innate immune host responses. Clinical trials have revealed its protective effect on sepsis, but lactoferrin dosage, duration, and role in the prevention of sepsis are still uncertain. OBJECTIVE: We aimed to establish the efficacy of bovine lactoferrin in the prevention of late-onset sepsis and to determine the optimal dose and method of administering bovine lactoferrin that may contribute to improvement in overall survival of low birth weight infants. METHODS: We will implement the study in 2 phases at the Aga Khan University Hospital. The first phase, which we have completed, was formative research. This phase mainly focused on a qualitative exploration of perceptions about feeding and caring practices of low birth weight newborns and a trial of improved practices for the preparation and administration of bovine lactoferrin to newborns. The second phase is a 3-arm double-blind randomized controlled trial. In this phase, we randomly allocated 2 different daily oral prophylactic doses of bovine lactoferrin (150 mg or 300 mg) and placebo to 300 low-birth weight neonates starting within the first 72 hours of birth and continuing for the first 28 days of life. RESULTS: The study protocol was approved by the Ethics Review Committee of Aga Khan University on August 16, 2017. Data collection began in April 2018 and was completed in September 2020. Data analyses are yet to be completed. We expect the results to be published in peer-reviewed journals by autumn of 2021. CONCLUSIONS: This intervention, if effective, has the potential to be translated into a safe, affordable, and widely utilized treatment to prevent sepsis and, subsequently, may improve the survival outcomes of low birth weight neonates in Pakistan and other low- and middle-income countries. TRIAL REGISTRATION: ClinicalTrials.gov NCT03431558; https://clinicaltrials.gov/ct2/show/NCT03431558. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/23994.
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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 | 0.034 | 0.032 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.050 | 0.008 |
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".