Adequacy of empirical antifungal therapy and effect on outcome among patients with invasive Candida species infections
Bibliographic record
Abstract
OBJECTIVES: Although inadequate antimicrobial therapy has been demonstrated in multiple studies to increase the risk for death in bacterial infections, few data investigating the effect of antifungal therapy on outcome of serious fungal disease are available. We sought to assess the adequacy of empirical therapy and its effect on mortality in invasive Candida species infections. METHODS: Population-based surveillance of all patients with Candida spp. cultured from blood and/or cerebrospinal fluid was conducted. Adequacy of empirical therapy was assessed according to published guidelines. RESULTS: During a 5 year period, 207 patients had an invasive Candida spp. infection identified; in 199 cases (96%) adequate data were available for assessment of treatment and outcome at hospital discharge. One hundred and three (52%) cases were due to Candida albicans, 44 (22%) were due to Candida glabrata and the remainder were due to other species. Between the time of culture draw and reporting of a positive culture, only 64 (32%) patients were treated with empirical therapy; this was deemed adequate in 51 (26%). Patients who received adequate empirical therapy had a significant decrease in crude mortality [14/51 (27%) versus 68/148 (46%); risk ratio 0.60 (95% confidence interval 0.37-0.96); P = 0.02]. After adjusting for age and the need for intensive care unit admission in logistic regression analysis, the use of adequate empirical therapy was independently associated with a reduced risk for death [odds ratio 0.46 (95% confidence interval 0.22-1.00); P = 0.05]. CONCLUSIONS: Adequate empirical therapy is used in a minority of patients with invasive Candida spp. infections but is associated with improved survival.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".