Impact of hospital infections on patients outcomes undergoing cardiac surgery at Santa Casa de Misericórdia de Marília
Bibliographic record
Abstract
OBJECTIVE: This study aimed to determine the incidence of nosocomial infections, the risk factors and the impact of these infections on mortality among patients undergoing to cardiac surgery. METHODS: Retrospective cohort study of 2060 consecutive patients from 2006 to 2012 at the Santa Casa de Misericórdia de Marília. RESULTS: 351 nosocomial infections were diagnosed (17%), 227 non-surgical infections and 124 surgical wound infections. Major infections were mediastinitis (2.0%), urinary tract infection (2.8%), pneumonia (2.3%), and bloodstream infection (1.7%). The in-hospital mortality was 6.4%. Independent variables associated with non-surgical infections were age > 60 years (OR 1.59, 95% CI 1.09 to 2.31), ICU stay > 2 days (OR 5, 49, 95% CI 2.98 to 10, 09), mechanical ventilation > 2 days (OR11, 93, 95% CI 6.1 to 23.08), use of urinary catheter > 3 days (OR 4.85 95% CI 2.95 -7.99). Non-surgical nosocomial infections were more frequent in patients with surgical wound infection (32.3% versus 7.2%, OR 6.1, 95% CI 4.03 to 9.24). Independent variables associated with mortality were age greater than 60 years (OR 2.0; 95% CI 1.4 t o3.0), use of vasoactive drugs (OR 3.4, 95% CI 1.9 to 6, 0), insulin use (OR 1.8; 95% CI 1.2 to 2.8), surgical reintervention (OR 4.4; 95% CI 2.1 to 9.0) pneumonia (OR 4.3; 95% CI 2.1 to 8.9) and bloodstream infection (OR = 4.7, 95% CI 2.0 to 11.2). CONCLUSION: Non-surgical hospital infections are common in patients undergoing cardiac surgery; they increase the chance of surgical wound infection and mortality.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".