Epidemiology, Clinical, and Microbiological Characteristics of Multidrug-Resistant Gram-Negative Bacteremia in Qatar
Bibliographic record
Abstract
Antimicrobial resistance is a global healthcare threat with significant clinical and economic consequences peaking at secondary and tertiary care hospitals where multidrug-resistant Gram-negative bacteria (MDR GNB) lead to poor outcomes. A prospective study was conducted between January and December 2019 for all invasive bloodstream infections (BSIs) secondary to MDR GNB in Qatar identified during routine microbiological service to examine their clinical, microbiological, and genomic characteristics. Out of 3238 episodes of GNB BSIs, the prevalence of MDR GNB was 13% (429/3238). The predominant MDR pathogens were Escherichia coli (62.7%), Klebsiella pneumoniae (20.4%), Salmonella species (6.6%), and Pseudomonas aeruginosa (5.3%), while out of 245 clinically evaluated patients, the majority were adult males, with the elderly constituting almost one-third of the cohort and with highest observed risk for prolonged hospital stays. The risk factors identified included multiple comorbidities, recent healthcare contact, previous antimicrobial therapy, and admission to critical care. The in-hospital mortality rate was recorded at 25.7%, associated with multiple comorbidities, admission to critical care, and the acquisition of MDR Pseudomonas aeruginosa. Resistant pathogens demonstrated high levels of antimicrobial resistance but noticeable susceptibility to amikacin and carbapenems. Genomic analysis revealed that Escherichia coli ST131 and Salmonella enterica ST1 were the predominant clones not observed with other pathogens.
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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) | 0.000 | 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 | 0.000 |
| 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".