Short course of intravenous antibiotics in the treatment of uncomplicated proven neonatal bacterial sepsis: A systematic review
Bibliographic record
Abstract
AIM: To evaluate the efficacy and harms of a short (7-10 days) compared with a standard (10-14 days) duration of antibiotics in culture-proven neonatal sepsis for reducing all-cause mortality, treatment failure and duration of hospitalisation. METHODS: Medline, EMBASE and Cochrane CENTRAL were searched for randomised trials. RESULTS: We included five studies, all conducted in India (447 infants with a gestational age greater than 32 weeks). Except for one study, all studies were at high risk of bias. All-cause mortality was reported in three studies with only one death reported in the standard duration regimen arm (243 patients, very low certainty). A meta-analysis showed no evidence of the effect on treatment failure (RR of 1.47 [95% CI 0.48-4.50], 440 patients, five studies, very low certainty) of short-term antibiotics. Short-term antibiotic regimen shortened the duration of hospitalisation by 4 days (mean difference of -4.04 days [95% CI -5.47 to -2.61]; 4 studies; 371 patients; very low certainty). CONCLUSION: Among studies focused on infants born with a gestational age greater than 32 weeks, short-term administration of antibiotics may shorten the duration of hospitalisation, but the evidence is very uncertain. The evidence on other predefined outcomes is very uncertain to draw definite conclusions.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".