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Drug-use patterns in an intensive care unit of a hospital in Iran: an observational prospective study

2010· article· en· W1504566048 on OpenAlex
Mahkam Tavallaee, Fanak Fahimi, Shirin Kiani

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Pharmacy Practice · 2010
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMedicineObservational studyMedical prescriptionIntensive care unitOdds ratioConfidence intervalLogistic regressionEmergency medicineProspective cohort studyAntibioticsDrugIntensive care medicineInternal medicinePharmacology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.212
GPT teacher head0.488
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it