Antibiotic Use for Sepsis in Hospitalized Neonates in Botswana: Factors Associated with Guideline-Divergent Prescribing
Bibliographic record
Abstract
In low- and middle-income countries, where antimicrobial access may be erratic and neonatal sepsis pathogens are frequently multidrug-resistant, empiric antibiotic prescribing practices may diverge from the World Health Organization (WHO) guidelines. This study examined antibiotic prescribing for neonatal sepsis at a tertiary referral hospital neonatal unit in Gaborone, Botswana, using data from a prospective cohort of 467 neonates. We reviewed antibiotic prescriptions for the first episode of suspected sepsis, categorized as early-onset (EOS, days 0-3) or late-onset (LOS, >3 days). The WHO prescribing guidelines were used to determine whether antibiotics were "guideline-synchronous" or "guideline-divergent". Logistic regression models examined independent associations between the time of neonatal sepsis onset and estimated gestational age (EGA) with guideline-divergent antibiotic use. The majority (325/470, 69%) were prescribed one or more antibiotics, and 31 (10%) received guideline-divergent antibiotics. Risk factors for guideline-divergent prescribing included neonates with LOS, compared to EOS (aOR [95% CI]: 4.89 (1.81, 12.57)). Prematurity was a risk factor for guideline-divergent prescribing. Every 1-week decrease in EGA resulted in 11% increased odds of guideline-divergent antibiotics (OR [95% CI]: 0.89 (0.81, 0.97)). Premature infants with LOS had higher odds of guideline-divergent prescribing. Studies are needed to define the causes of this differential rate of guideline-divergent prescribing to guide future interventions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".