A Comparative Analysis of Antibiotic and Antiviral Drug Use in Pediatrics and ObstetricsGynecology over 12 years (2000-2001, 2005-2006 and 2012-2013) using Defined Daily Doses and Days of Therapy
Bibliographic record
Abstract
Background: There are limited data published about antibiotics and antiviral consumption in terms of Defined Daily Doses (DDD) and Days Of Therapy (DOT) in Pediatrics and ObstetricsGynecology. Objectives: To characterize and quantify antimicrobial drug use as DDD/1000 patient-days per molecule, DOT/1000 patient-days per molecule, and mean dose (mg/kg/day) per molecule over a twelve-year period and explore the changes over time in Pediatrics and Obstetrics-Gynecology. Methods: Retrospective, cross-sectional, descriptive study, in a mother-child University Hospital Center, with 400 pediatric beds and 100 obstetric-gynecology beds. All inpatient (in Pediatrics and Obstetrics-Gynecology) who received one of the 51 authorized antibiotics or one of the 9 authorized antivirals on the institution’s local formulary in 2000-2001, 2005-2006 and 2012-2013 were included. Prescriptions from the emergency room and outpatient clinics were excluded. Data were extracted from the patients’ computerized medication profiles. We calculated DDD/1000 patient-days per molecule, DOT/1000 patient-days per molecule overall and for each molecule. The mean dose in mg/kg/day was calculated for each molecule for the ranges: ≤ 1.5 kg; > 1.5-5 kg; > 5-15 kg; > 15-30 kg; and > 30 kg. Results: Over 12 years: there was a 1.67-fold increase for antibiotics and a 3.77-fold increase for antivirals in the overall number of DDD/1000 patient-days. There was a 1.73-fold increase for antibiotics and a 2.57-fold increase for antivirals in the overall number of DOT/1000 patientdays. It reveals increases in dosage regimens for amoxicillinclavulanic acid, gentamicin, piperacillin, piperacillin-tazobactam, and ticarcillin-clavulanic acid. Nevertheless, azithromycin and erythromycin dosage regimens decrease. The antivirals data reveals no predictable tendencies. Conclusion: This retrospective, cross-sectional, descriptive study reported the use of anti-infectious drugs at a mother-child hospitalover a 12-year time period. Both the overall numbers of DDD/1000 patient-days and the DOT/1000 patient-days increased. It should be monitored on a continuous basis by antimicrobial stewardship program in healthcare settings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".