Mortality associated with third generation cephalosporin-resistance in <i>Enterobacteriaceae</i> infections: a multicentre cohort study in Southern China
Bibliographic record
Abstract
ABSTRACT Background Emerging third generation cephalosporin-resistant Enterobacteriaceae (3GCR-EB) pose a global healthcare concern. We assessed excess mortality in patients infected with 3GCR-EB compared to patients infected with third-generation cephalosporin-susceptible Enterobacteriaceae (3GCS-EB). Methods The study cohort comprised all inpatients with a community-onset or healthcare-associated infection caused by Enterobacteriaceae in three tertiary-care public hospitals in 2017. Excess in-hospital mortality was assessed using competing risk survival models, adjusting for baseline patient characteristics. Results Of 2,343 study patients (median age 60 years; 45.2% male), 1,481 (63.2%) had 3GCS-EB and 862 (36.8%) 3GCR-EB infection. 494 (57.0%) 3GCR-EB isolates were co-resistant to fluoroquinolones and 15 (1.7%) co-resistant to carbapenems. In-hospital mortality was similar in the 3GCS-EB and 3GCR-EB groups (2.4% vs. 2.8%; p=0.601). No increase in the hazard of in-hospital mortality was detected for 3GCR-EB infections compared to 3GCS-EB infections (sub-distribution hazard ratio [HR] 0.80; 95%CI, 0.41 - 1.55) in multivariable analysis adjusting for patient age, sex, intensive care admission, origin of infection and site of infection. Analysis of cause-specific hazards showed that 3GCR-EB infections significantly decreased the daily rate of hospital discharge (cause-specific HR=0.84; 95%CI, 0.76 - 0.92) thereby leading to lengthier hospitalizations. Conclusion Third-generation cephalosporin resistance in Enterobacteriaceae infection per se was not associated with increased in-hospital mortality in this study. However, 3GCR-EB infections were seen to place significant healthcare burden by increasing the length of hospitalization compared to 3GCS-EB infections.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".