198Evaluation of Antibiotic Use in the Neonatal Intensive Care Unit (NICU): from an Antimicrobial Stewardship (AMS) perspective
Bibliographic record
Abstract
Background. Neonatal infections result in significant morbidity, mortality and increased health care costs. Antibiotics are commonly prescribed to infants hospitalized in the Neonatal Intensive Care Unit (NICU). Broad spectrum antibiotic exposure has been associated with emergence of resistant organisms and disruption in the development of normal flora. There is paucity of data evaluating the appropriateness of antibiotic use in the NICU, based on Centers for Disease Control and Prevention (CDC) 12-Step Guidelines to Prevent Antimicrobial Resistance. Methods. A retrospective audit of antibiotic use at a tertiary perinatal centre covering four million population was conducted during July 2010 – June 2013. Our objective was to assess the practice of antibiotic therapy for compliance to the recommendations of the CDC 12-step campaign. Results. We audited vancomycin, meropenem and linezolid use in the NICU. Empirical use >3-day without appropriate specimens collected, or utilization despite narrower spectrum antibiotic available, were considered inappropriate use. Conclusion. The CDC 12-Step Campaign is feasible in the NICU setting. Inappropriate antibiotic prescriptions are not uncommon in relation to the use of meropenem, vancomycin and linezolid. Attention should be focused on timely streamlining of these antimicrobials and collection of appropriate microbiologic specimens. Disclosures. All authors: No reported disclosures.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".