Surveillance of antimicrobials use in neonatal hospitals with alternative DDD methods
Bibliographic record
Abstract
One of the most common problems in neonatal hospitals is the choice of metrics of drugs usage in neonatology, especially use of antibiotics. This study represents our experience in quantifying antibiotic use in a Level-III neonatal intensive care units (NICUs) implementing two methods: days of therapy (DOT) and length of therapy (LOT). Our objective was to quantify antibiotic use by most common metrics of Antimicrobial Stewardship Programs in NICUs of Europe, USA and Canada in order to standardize antibiotic use measures in Russian neonatal hospitals. The study included analysis of medical records for infants who were treated in two NICUs in 2014. Our study demonstrated the suitability of the method for pharmacological monitoring and Antimicrobial Stewardship Program in neonatal hospitals despite some limitations. It permits to quantify and compare all antibiotic use and antibiotic prescription rate between departments and different neonatal hospitals.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".