Combined use of metagenomic sequencing and host response profiling for the diagnosis of suspected sepsis
Bibliographic record
Abstract
ABSTRACT Background Current diagnostic techniques are inadequate for rapid microbial diagnosis and optimal management of patients with suspected sepsis. We assessed the clinical impact of three powerful molecular diagnostic methods. Methods With blood samples from 200 consecutive patients with suspected sepsis, we evaluated 1) metagenomic shotgun sequencing together with a Bayesian inference approach for contaminant sequence removal, for detecting bacterial DNA; 2) viral capture sequencing; and 3) transcript-based host response profiling for classifying patients as infected or not, and if infected, with bacteria or viruses. We then evaluated changes in diagnostic decision-making among three expert physicians by unblinding the results of these methods in a staged fashion. Results Metagenomic shotgun sequencing confirmed positive blood culture results in 14 of 26 patients. In 17 of 200 patients, metagenomic sequencing and viral capture sequencing revealed organisms that were 1) not detected by conventional hospital tests within 5 days after presentation, and 2) classified as of probable clinical relevance by physician consensus. Host response profiling led at least two of three physicians to change their diagnostic decisions in 46 of 100 patients. The data suggested possible bacterial DNA translocation in 8 patients who were originally classified by physicians as noninfected and illustrate how host response profiling can guide interpretation of metagenomic shotgun sequencing results. Conclusions The integration of host response profiling, metagenomic shotgun sequencing, and viral capture sequencing enhances the utility of each, and may improve the diagnosis and management of patients with suspected sepsis.
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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 | 0.001 | 0.003 |
| 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".