Reducing Infectious Morbidity and Accelerating Linear Growth among Low Birth Weight Infants with Hand Sanitizers and Nutrition and Hygiene Education in Rural Bangladesh
Bibliographic record
Abstract
Objective To investigate if use of water‐based hand sanitizer along with nutrition and hygiene education can prevent linear growth faltering among term, low birth weight (TLBW) infants in Bangladesh. Methods Community‐based, cluster randomized, controlled trial using 48 clusters randomly assigned to two groups: i) Nutrition and Hygiene Education (NHE) plus directed use of Benzalkonium Chloride ‐based Hand Sanitizers (HS) by mothers and other family members; ii) NHE only. We followed 467 TLBW infants for 6 months with weekly morbidity and monthly anthropometry. Results At enrolment, mean ± SD anthropometric measures were comparable between HS and NHE groups (birth weight: 2.27±0.2 vs. 2.26±0.2 kg; and birth length: 44.2±1.8 vs. 43.8±1.9 cm). Although results at 1 month postpartum showed no difference in anthropometry between groups, infants in the HS group had reduced rates of skin lesions (12.6% vs.19.4%); infected umbilicus (0.8% vs. 1.4%) and eye infections (0.0% vs. 2.1%) compared to the NHE group. Reported household expenditure on care seeking due to neonatal illness was significantly less in the HS group than that of the NHE group ( P <0.009). These preliminary findings suggest a positive effect of hand sanitizer on morbidity at 1 month in term, low birth weight, rural Bangladeshi infants. Funding: Bill & Melinda Gates Foundation to FHI 360, through the Alive & Thrive Small Grants Program managed by UC Davis . Grant Funding Source : Alive and Thrive Small Grants Program, managed by UC Davis
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".