Antibiotic Consumption in the Latvian Teaching Hospital 2000-2008
Bibliographic record
Abstract
Antibiotic Consumption in the Latvian Teaching Hospital 2000-2008 Antibiotics are one of the most commonly used drugs in hospital care and significantly contribute to healthcare costs. Recently, there has been significant interest raised on the environmental impact of antibiotic use, in particular on how it effects resistance selection pressure. Rapid global spread of multiresistant bacteria requires improved understanding on how changes in antibiotic use affect resistance selection. We present a unique study on antibiotic consumption and its trends over an eight-year period. Data were obtained from the pharmaceutical database system. The study period extended from January 2000 through December 2008. Antibiotic use was expressed as a rate — defined daily doses (DDD) per 100 patient days (DDD/100) in a quarter year. The total amount of antibiotics used for systemic treatment at the beginning of the period (first quarter of 2000) was 38.7 DDD per 100 bed days and increased to 72.6 DDD per 100 bed days (r = 0.81) by the 4th quarter of 2008. Despite variability during the study period, a significant trend was observed with an average increase of 0.97 (95% CI: 1.2; 3.2) per quarter. Penicillin was the most common antibiotic group used at the hospital in the study period and demonstrated the greatest increase in consumption (r = 0.92). The consumption rates of fluoroquinolones were high and also showed a significant increase (r = 0.76). We observed a significant increase of antibiotic consumption in our hospital during the study period, which lacked a clear explanation. This increase was mostly due to increased use of amoxicillinum/enzyme inhibitor and ceftriaxone. Analysis of consumption should be continued to assess the impact of educational interventions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".