Prescription of Antibiotics in Intensive Care Units in Latin America: An Observational Study
Bibliographic record
Abstract
A one-day point prevalence study to investigate the patterns of antibiotic use was undertaken in 43 latin American (LA) intensive care units. Of 510 patients admitted, 231 received antibiotic treatment on the day of the study (45%); in 125 cases (54%) due to nosocomial-acquired infections. The most frequent infection reported was nosocomial pneumonia (43%). Only in 122 patients (53%) were cultures performed before starting antibiotic treatment. 33% of the isolated microorganisms were enterobacteriaceae (40% extended-spectrum beta-lactamase-producing), 23% methicillin-resistant Staphylococcus aureus and 17% carbapenems-resistant non-fermentative Gram-negatives. The antibiotics most frequently prescribed were carbapenems (99/231, 43%); alone (60/99, 60%) or in combination with vancomycin (39/99, 40%). "Restricted" antibiotics (carbapenems, vancomycin, piperacillin-tazobactam, broad-spectrum cephalosporins, tigecycline, polymixins and linezolid) were most frequently indicated in severely ill patients (APACHE II score at admission >15, p=0.0007 and, SOFA score at the beginning of the antibiotic treatment >3, p=0.0000). Only 36% of antibiotic treatments were cultured-directed.Our findings help explain the high rates of multidrug-resistant pathogens in LA settings (i.e. ESBL-producing Gram-negatives) and the severity of the registered patients illnesses.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".