Management of term infants at increased risk for early-onset bacterial sepsis
Bibliographic record
Abstract
Early-onset neonatal bacterial sepsis (EOS) is sepsis occurring within the first 7 days of life. This statement provides updated recommendations for the care of term (≥37 weeks' gestational age) newborns at risk of EOS, during the first 24 hours of life. Maternal Group B streptococcus (GBS) colonization in the current pregnancy, GBS bacteriuria, a previous infant with invasive GBS disease, prolonged rupture of membranes (≥18 hours) and maternal fever (temperature ≥38°C) are the factors most commonly associated with EOS. These risk factors are additive; the presence of more than one factor increases the likelihood of EOS. At present, there is no laboratory test, including white blood cell indices, that has sufficient sensitivity to allow clinicians to safely rule out EOS. All unwell infants with clinical signs suggesting sepsis must be treated empirically with antibiotics, once cultures have been taken. The management of well-appearing, at-risk term infants depends on the number of risk factors (including maternal GBS colonization) and whether maternal intrapartum antibiotic prophylaxis for GBS was used. In some cases, management should be individualized. Careful assessment and observation of these at-risk infants are a fundamental component of appropriate care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".