Usefulness of Defined Daily Dose and Days of Therapy in Pediatrics and Obstetrics-Gynecology: A Comparative Analysis of Antifungal Drugs (2000–2001, 2005–2006, and 2010–2011)
Bibliographic record
Abstract
OBJECTIVES: The objective was to describe antifungal drug use by using the number of defined daily doses (DDD)/1000 patient-days per antifungal, the number of days of therapy (DOT)/1000 patient-days per antifungal, and the mean dose in mg/kg/day per antifungal during a 10-year period. METHODS: Retrospective, cross-sectional, descriptive study, in a mother-child university hospital center, with 400 pediatric beds and 100 obstetrics-gynecology beds. All inpatients who received 1 of the 7 authorized antifungals on the institution's local formulary in 2000-2001, 2005-2006, or 2010-2011 were included. Prescriptions for emergency department and outpatient clinics were excluded. The data were extracted from the patients' computerized medication profiles linked to patient admission, discharge, and transfer data. The DDD, DOT, and the mean dose in mg/kg/day were calculated for each antifungal and overall. RESULTS: There was a 2.97-fold increase in the overall number of DDD/1000 patient-days, from 14.8 in 2000-2001 to 37.5 in 2005-2006 and 43.9 in 2010-2011. There was a 2.97-fold increase in the overall number of DOT/1000 patient-days, from 22.8 in 2000-2001 to 50.3 in 2005-2006 and 67.8 in 2010-2011. CONCLUSIONS: It can be difficult to compare the use of antifungal drugs among institutions, owing to numerous factors, but it gives an idea about the consumption outside the studied center. Moreover, these ratios help to evaluate the use of antifungals within a same institution. These data could be correlated among others, with resistance patterns, in order to improve our daily practice concerning antifungal prescription.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".