Microorganisms causing urinary tract infections in a teaching hospital in northeastern Brazil / Microorganismos causantes de infecciones del tracto urinario en un hospital universitario en el noreste Brasil / Microorganismos causadores de infecções...
Bibliographic record
Abstract
Objective: to study the UTI-causing bacteria frequency and sensitivity profiles in a teaching hospital in northeastern Brazil. Method: A retrospective cross-sectional study was conducted based on the review of 279 patients for whom uroculture and urinary catheter cultures were routinely processed in the Microbiology Laboratory of Sao Vicente de Paulo Hospital. Results: For the catheter culture group, the most frequent microorganism was Staphylococcus epidermidis (47%), while in urine culture group Escherichia coli was the microorganism most frequently isolated (52%). E. coli showed 76.46%, 70%, and 86.36% resistance to ampicillin, amoxicillin and Sulfamethoxazole/trimethoprim respectively. S. epidermidis showed high resistance to most drugs used, demonstrating that these drugs should not be used to treat UTIs in this institution. Conclusion: This study represents the first study evaluating bacterial resistance in this institution and since data involving epidemiological surveillance and microbiological are limited in this region and due to its importance in the national context, the results may reflect important information to the body of research/data on bacterial resistance in the world.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".