Antibiotics Administration and Risk of Fungal Infection in Burns
Bibliographic record
Abstract
Exposure to antibiotics is a major risk factor for fungal infections (1). Alterations of the gut and skin flora allows progressive colonization by Candida sp, which may result in an invasive candidiasis (2). The impact varies among different classes of antibiotics, and cephalosporins have the more pronounced effect in non burned patients. We explore the impact of exposure to different classes of antibiotics on candida colonization in burned patients. Using our prospective research clinical database, we included all burned patients consecutively admitted from July 1st 2002 to September 20th 2005, until discharge or first candida identified. Antibiotics were classified according to the WHO criteria. Systematic screening cultures were routinely performed in all patients. The incidence of colonization was compared according to the type of antibiotics used and expressed as odd ratios and we calculated the number of patients to be exposed to antibiotics to become colonized. Over 3 years, we included 404 burned patients. 280 (69%) received antibiotics and 75 (27% of them) become colonized by Candida, as compared to no colonization in those not exposed to antibiotics (OR 45.1). The risk was correlated with the antibiotic selection pressure ((Figure). All classes were associated with significant risk of candida colonization, except cephalosorins (Table)
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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 | 0.004 |
| 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 | 0.001 |
| 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".