Modeling the Impact and Cost of a Culture-Dependent Molecular Test for Antimicrobial Resistance in Resource-Limited Settings
Bibliographic record
Abstract
BACKGROUND: Limited diagnostic access in resource-limited settings contributes to poor health outcomes among bacterial sepsis patients and the spread of antimicrobial resistance (AMR). Molecular diagnostic profiling of AMR may enable faster targeting of antibiotic therapies, improving clinical outcomes, reducing AMR development, and saving costs. METHODS: We modeled the impact of a culture-dependent molecular test for pathogen identification and resistance testing among hospitalized bloodstream infection patients to guide effective and cost-efficient implementation of these tools. We evaluated patient mortality, antibiotic use, hospital-associated infections, hospital days, and costs under the standard of care (empiric therapy, blood culture, phenotypic susceptibility testing) compared with molecular diagnostics by varying culture and susceptibility testing coverage, culture turnaround time, and AMR prevalence. RESULTS: The greatest impact of the molecular test occurred with 100% diagnostic coverage, shorter culture turnaround time, and high AMR prevalence, reducing up to 6% of deaths (interquartile range [IQR], 0%-12.1%), 5% of hospital days (IQR, 0.1%-10.7%), and 21% of days on inappropriate antibiotic therapy (IQR, 18.2%-24.4%). The minimum cost per molecular test performed, offset by cost savings, ranged from $109 in India to $585 in South Africa across all modeled scenarios. CONCLUSIONS: In high AMR burden settings with blood culture infrastructure supporting fast turnaround times, a molecular test could improve health outcomes of bloodstream infection. Impact is limited by delayed turnaround times and the effectiveness of empiric therapy. Molecular diagnostics implemented at $100 or less can generate healthcare-system cost savings, supporting their adoption to improve health outcomes and reduce AMR while remaining cost-neutral.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".