Antibiotic stewardship in neonates: challenges and opportunities
Bibliographic record
Abstract
Antibiotics are the most frequently used medications in neonates.The neonatal intensive care unit (NICU) houses immunocompromised newborn who are highly susceptible to overwhelming infections.Early and decisive treatment with powerful antibiotics for neonates with suspected infection is the preferred clinical doctrine owing to the fear of potentially disastrous consequences.The high associated mortality from the infections leads neonatal care providers to initiate empirical antibiotic therapy.However, antibiotics are often continued in clinical situations in which a clear indication or benefit has not been demonstrated.There is increasing evidence of adverse outcomes, such as increase in mortality, various morbidities, and even short-term neurodevelopmental outcomes from prolonged antibiotic use without evidence of sepsis in neonates (1,2).Lu et al. recently shared their experience with reduction in the use of unnecessary antibiotics in their 150-bed outborn tertiary NICU in an article published in the journal Critical Care Medicine (3).The study team implemented a multi-disciplinary antibiotic stewardship program (ASP) named "Smart Use of Antibiotics Program" or "SMAP" from June 2016 onwards, targeting prolonged and unnecessary use of antibiotics, as part of their Joint Commission International accreditation process.A multidisciplinary team was established to look at the strategies to achieve the goal, focusing on audit-and-feedback, prior authorization, and point-of-prescription interventions.They categorized antibiotic use into three, namely non-restricted (e.g., ampicillin), restricted (e.g., third-generation cephalosporin),
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.057 | 0.034 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".