Drug-use patterns in an intensive care unit of a hospital in Iran: an observational prospective study
Bibliographic record
Abstract
OBJECTIVES: the aim of this study was to evaluate drug-use patterns, investigate the factors influencing patient outcome, and determine the cost of drugs utilized in the intensive care unit (ICU). METHODS: in an observational prospective study, drug prescriptions for 113 patients admitted to the ICU of a hospital in Iran were recorded. The cost of drugs in ICU and the entire hospital was also calculated. Descriptive analysis and logistic regression were used to present the results. KEY FINDINGS: the mean age of patients was 50.3 years (SD = 20.4). The average ICU stay was 6 days. The mean length of stay was significantly lower in surgical patients compared to medical patients (odds ratio (OR) = 0.91, 95% confidence interval (CI) 0.84-0.97). Mortality rate was significantly higher among medical patients (OR = 10.5, 95% CI 3.7-29.8). There was a significant positive association between the total number of prescribed drugs or antibiotics received by patients and mortality. Patients received an average of 8.2 drugs at admission, 10.1 drugs during the first 24h and an average of 14.6 drugs over their entire stay at the icu. among drug groups, antibiotics and sedatives were most ordered drugs in icu. CONCLUSIONS: antibiotics are responsible for the majority of ICU drug costs. Appropriate selection of antibiotics in terms of type, dose and duration of therapy could tremendously reduce the expenses in hospitals without negatively influencing the quality of healthcare.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".